But it isn't seeing, right? I mean, seeing a digital image of a thing is totally different from seeing the actual thing.
A digital image is a representation/abstraction/map kind of thing. And then a little bit of mental shorthand inside your head makes a connection and declares the thing and the representation-of-the-thing to be one and the same.
But it isn't the same thing. Not by a million miles.
It’s also the name of the software they, and others, use. Though I guess the actual software has the generic name "Analyzer": https://corp.dxomark.com/analyzer/
I'm not very convinced. It looks like they are doing some processing, maybe special to the moon but it looks more like some form of sharpening or contrast boosting than adding detail. In all of the examples it seems that there is information in the original (dark spots) that are getting boosted.
It would be interesting to see this tried on a source image that isn't the moon. Just white with a few dark spots. Does it actually add in completely new craters, or just where there are existing smudges? Or do something like half of a moon photo and have white, does it add craters to the white side?
The OP tried to do this by changing the contrast but I failed to see any craters appearing where there wasn't already dark spots in the source photo.
It does seem strange that the OP is using an image of the moon to start and that they don't provide a still shot of the one where they modified the brightness levels to cause clipping. It doesn't really "drive the point home" as claimed.
Of course the answer to these may be that you need something moon-like enough to trigger the moon optimizations. But if that is the answer it would be interesting to see something that comes right up to the threshold where it either snaps in and out of these optimizations or two very similar images produce widely different results.
> In all of the examples it seems that there is information in the original (dark spots) that are getting boosted.
That was the point of the Gaussian blur. By blurring the source image of the moon before taking its picture, there was no information. The Gaussian blur destroys fine detail, by design.
The image enhancement applied by the Samsung phone is adding detail where there was none originally – not just detail that might be buried in the optics somewhere, but not there at all. It involves a computational model of what the target (here the moon) "should" look like and guesses at what it thinks are the blurry bits.
Gaussian blur is reversible with deconvolution - however that is almost certainly not what is happening here, as it’s fairly computationally expensive.
Seems at least as likely that the neural network would learn a generally-applicable approximate Gaussian deconvolution as a specifically-applicable Moon upscale.
But I don't see it adding any detail over the blur. It really just looks like it is boosting contrast a bit on top of the blur. I don't see anything like ridges or defined features appearing.
No, Gaussian blur does not "destroy" fine detail. It is still there it is just that the "volume" of it is turned down. You can put all the fine detail back again by applying the inverse filter.
Sensor quality in phones goes down, AI makes up for it because good sensors are expensive, but compute time in the cloud on Samsung owned servers is cheap. You take a picture on a crappy camera, and Samsung uses AI to "fix" everything. It knows what stop signs, roadways, busses, cars, stop lights, and more should look like, and so it just uses AI to replace all the textures.
Samsung sells what's on the image to advertisers and more with the hallucinated data. People can't tell the difference and don't know. They "just want a good looking picture". People further use AI to alter images for virtual likes on Tiktok and Insta.
This faked data, submitted by users as "real pics in real places" is further used to train AI models that all seem to think objects further away have greater detail, clarity, and cleanliness than they should.
You look at a picture of a park you took, years before, and could have sworn the flowers were more pink, and not as red. You are assured, by your friend who knows it all, that people's memories are fallible; hallucinating details, colors, objects, sizes, and more. The image, your friend assures you further? "Advanced tech captured its pure form perfectly".
And thus, everyone will demand more clarity, precision, details, and color where their eyes don't remember seeing.
Basically this.. As "neat" as AI "improvement" is, I don't think it has any actual value, I can't come up with any use-case where I can accept it. "Make pictures look good by just hallucinating stuff" is one of the harder ones to explain, but you did it well..
Another thing, pictures for proof and documentation, maybe not when they're taken but after the fact, for historical reasons, or forensics.. We can't have every picture automatically compromised as soon as it's taken. (Yes, I know that photoshop is a thing, but that's a very deliberate action, which I believe it should be)
I think the main use case is "I'm a crummy photographer and all I want is something to remind me that I was there" and "Look at my cat. Look! Look at her!"
That's me. I'm a lousy photographer, as evidenced by all of the photos I shot back when film actually recorded what you pointed it at. My photography has been vastly improved by AI. It hasn't yet reached the point of "No, you idiot, don't take a picture of that. Go left. Left! Ya know what, I'm just gonna make something up," but it should.
I imagine there will remain a use case for people who can actually compose good shots. For the remaining 99% of us, we'll use "Send the camera on vacation and stay home; it's cheaper and produces better pictures" mode.
That would actually be a useful feature, I'm aiming the camera but based on what makes "good professional" photos, it suggests "move to the left so you frame the picture well" or "those two people should be more spread out so its not one person with two heads" etc, kindof like lane warnings on cars.
I'm a decent photographer and still use my phone for this. It's good enough, and can even skip the AI stuff if I want to. Or even better: I can keep the AI stuff in the raw and edit its impact on the final photo later.
"I'm a crummy photographer and all I want is something to remind me that I was there"
This is fine, and I can take good shots but at the same time? I only care about this level of shot most of the time too!
But then instead of a 20MP image, which:
* takes more space, and ergo, more flash drive space
* more space to store, to backup, to send
* is made 20MP by inserting fake data
Why not have a 2MP image, which is real, and let people's end-use device "fix" it? Because all that post processing can be done when 2x or 4x the view size, too!
Because advertising.
And that's sad. We'd rather think we have a better pic, and destroy the original.
And the space thing is real. Because, that same pic gets stored in gmail with 20 people, backed up, kept in all the devices, and so on!
And the LOL of it all, is that I bet when it is uploaded to facebook... it gets downsized!
edit: in fact, my email app allows me to resize on email, so I downsize that too! Oh, those poor electrons.
This is like Huffman compression on your photos but the AI companies created an exabyte size dictionary and now you can store your photo in a few kilobytes.
The key to all modern "AI" bullshit (ChatGPT, StableDiffusion etc) is that they're just exquisite lossy compressors, where a good-enough perceptual loss function has been learned automatically.
There's nothing wrong with that until people start assuming they're lossless (like Huffman encoding) which they definitely are not. Unfortunately the general public doesn't understand the difference.
As a kid I was taking a photo in a tourist spot with a film camera and standard 50mm lens. An elderly local guy grabbed me by the shoulder as I framed the photo. We shared no common language and he (not so gently) pulled me over to where I should stand to get the better shot.
You don't need AI for taking better photos, for most people the phone just automatically taking a burst/video and picking a frame out for the still or stacking frames would be plenty. Lots of photos suck because of shit lighting. A camera intelligently stacking frames would fix a lot of people's photos.
Interestingly enough one of the reason Sonys flagships perform really badly in comparisons is because they are weak at computational photography. So even when the sensor is great it looks too real, which people don't like.
Yeah I've a phone with a great camera, nature shots are great, but people don't like themselves in these photos. When pressed they talk about the defects on skin and theets and eye position... Their phone beauty filters created in their mind a fake mental image of themselves and they dissociate from their real images.
It's weird. My mom brand fidelity is because Huawei specific algorithm is part of her self.
Good for situations where you aren’t expecting or care about realism in this detail. AI hallucinations will be amazing for entertainment, especially games.
I want game content generation by AI, like for dungeon generation in an ARPG - it likely won’t be as good as hand crafted level by a developer but it should be more interesting than the current techniques where prefab pieces of a dungeon are randomly put together.
The term has been around in this context since at least 2018[1], and indeed I have chat logs from 2019 talking about how mtl [machine translation] hallucinates, so no, this has been what people have been calling it for a while now. Perhaps what you're seeing is just rising awareness that this is a weakness of current-gen ML models, which is great, now even monoglots get to feel my pain :V
How about using AI for sensor fusion when you have images from multiple different kinds of lenses (like most smartphones today)? I was under the impression this was the main reason why AI techniques became popular in smartphone cameras to begin with
I'm not aware of much fusion happening between different lenses (although I saw an article using that for better portrait mode bokeh), but AI is used to stack multiple images from the same sensor. You can do de-noise, HDR and other stacking stuff with clever code, but AI just makes it better.
I think it's neutral.. Just as incriminating as true photos can be (at least there might be some moral highground if you're into that sorta stuff), for AI faked pictures.. You may have no choice but be incriminated by photos that lie..
I like the story, but I think people will notice pretty quickly as almost everyone reviews their photos right after taking them (so they can compare them with what they see in reality)
True, its just a fun story. This reddit post makes it clear, though, that while people will review the images carefully, they may still not be able to accurately determine differences.
Just take the story above with one more minor step: You snap a pic of the park, briefly glanced at it to make sure it wasn't blurry (which the AI would have fixed anyway) or had an ugly glare (it did, the AI fixed it) or worse a finger (the AI also fixed that).
You're satisfied the image was captured faithfully and you did a good job holding your plastic rectangle to capture unseen sights. You didn't look closely enough to notice all the faked details, because they were so good.
This fake moon super enhance? It already proves people will fall for it. I could easily see people not realizing AI turned the flowers in the picture more red, or the grass just a little too green, etc.
The story has to get stretched a lot to imagine a total dystopia, but its possible.
People like digital mirrors for all their benefits: lighter, more features, music and weather all on one "mirror". You can have voice chats, see how you might look with various makeup/styles/haircuts. This digital mirror gets so popular, and (undermining myself here a bit) high enough quality, that people want these new cool wall screens instead.
And you betcha, AI is of course going to be added. Take insta pics without needing to hold a phone, then apply filters all in one with your voice! Some people take dozens of photos, forgetting briefly how they look.
Now, people are used to every day seeing themselves in the digital mirror with minor touchups for how they would look if they used some sponsored makeup. They used to do it daily so it would match, but kept forgetting as it always showed the improved version (with a small icon saying, you are 40% matched to the predicted image!).
Some 20-something walking around in Chicago passes The Bean, and realizes they don't look quite the same as they did in their home mirror. They take out their phone to take a pic of themselves, which is of course synced to their mirror with the same "makeup enhancement suggestions", still warning them it doesn't match.
They put away their phone, confident in the knowledge The Bean is just a dirty and distorted mirror, which is why they don't look as good. The camera has always been trustworthy. Why doubt it today?
(again, fun story, I don't think this is likely. Just plausible for some people).
Iphones already HDR the crap out of their photos. Saturation put to max levels for that pop, colors looking only vaguely like they really did.
The contrast between what my camera raw with a stock profile puts out and what my iphone puts out is striking, and it's very clear the iphone's version of reality is optimized for Instagram and maximum color punch at the cost of looking real.
Thing is, that's what people like. So that's what we're getting.
Of course they do. Why would they want lifelike photos? To remind them of how utterly crap things actually are? No, people want to have that idea of what life was like. Sharing a lifelike image on socials would get laughed off the platform. enhance, Enhanced, ENHANCE get all the likes
doesn't require one to be British to be thoroughly unimpressed with a photo and want to enhance to improve on the situation to be more in agreement with one's imagination.
You've been able to make "unlifelike" photos ever since you could adjust the aperture, white balance, focal length, and choose the framing. How many times have you seen the Pyramids at Giza in pictures and films? How many of those times were framed to include the nearby city slums and dumped rubbish?
Not outright saying it (and not you, your comment was just a place to hang another comment off) but parent comments in this chain saying "people already fall for it" about computer adjusted photos and software might adjust how "red the flowers are" or how "green the grass is", and the parent comment saying "you can turn all this off and get RAWs" - as if they believe there is some objective truth which RAWs capture and which cameras used to show that they now don't show because of software post-processing.
My point is that there never has been, cameras have always let you adjust the shot - including "how red the flowers are" by changing light source, film type, shadows, which contrasting other colours are nearby, etc.
It's like airbrushing and similar techniques, except automated. It's better than life (face-smoothing filters, eye-enhancing filters, whitening filters, HDR that blows the colors to make up for the tiny sensor and minuscule optics, major sharpening artifacts, smoothing texture, the list goes on and on)
With old-school photo processing--yes, in a darkroom--you could achieve unrealistic results. But it was a choice. That's not what you got when you sent your negatives to the Costco to get printed. That's akin to the results I get when I use my camera, especially when looking at jpgs straight-out-of-the-camera.
In contrast, we get modern cellphones doing incredible processing to almost arbitrarily replace content with what some algorithm feels you'll like better, whether or not it resembles reality.
I lament that it usually resembles beginner photographer work, where they've just discovered HDR tone-mapping, local contrast, global contrast, saturation, sharpening, and smoothing filters, and promptly slam every single one of them to the stops. I did it, and now I recognize it when I see it in cellphone pics my friends send me via imessage.
Been there, done that. I recognize the stigmata of saturation slammed to the stops and excessive use of HDR and local contrast.
The default is excessive editing now, probably because it helps cover up limitations of tiny sensors, small optics, and poor exposure due to poor technique.
The iPhone camera is tuned for realistic color unless you've left the style setting on Vibrant. I guess it doesn't have an "even less vibrant" style.
It does have higher than real contrast, but that's because images are 8-bit - if you don't try to fill the range, it's going to look low quality with banding artifacts.
Yeah, it exposes people in the foreground differently than the rest of the picture. That's still trying to fit them into 8-bit - basically it's trying to avoid the "black people don't activate soap dispensers" effect where dark skin isn't visible in a dark picture. (Also, if the user is only looking at a face in the picture, or if the face has a different lighting source than the rest of it, then it makes sense to calculate its exposure separately too.)
Larger cameras don't do that specific one as much ("dynamic range optimizer" or HDR programs do some of it), but they do care about skin in white balance and autofocus, and then photographers care when they're setting up flashes.
I guess I havent noticed that people do that for things other than selfies.
I generally just burst-mode-scan an area or scenery location and later that night, or when I add to Strava or wherever, I have an old school contact sheet (but with 60-80 images per thing) to look though. Then narrow it down to 5-10, pick the one or two I like best and discard the rest.
Or better, the AI improves your shitty snapshots so they come out great. Every shot is beautifully framed, perfect composition, correct light balance, worthy of a master photographer. You can point your camera any old way at a thing and the resulting photo will be a masterpiece.
The details don't quite correspond to reality; to get the framing right the AI inserted a tree branch where there wasn't one, or moved that pillar to the left to get the composition lined up. But who care? Gorgeous photo, right?
And the thing is, I don't think anyone would care. You'd get the odd weird comparison where two people take a photo of the same place and it looks different for each of them. And you'd lose the ability to use the collected photos of humanity to map the world properly.
I think it's fascinating. Reality is what we remember it to be. We can have a better reality easily ;)
That could be fine as long as there is either a way to turn all that off (or better a way to selectively turn parts of it off) or a separate camera app available that lets you do that.
It's the future. Something hit your self-driving hover car and left a small dent. To get your insurance to pay for fixing the dent you have to send them a photo.
Your camera AI sees the dent as messing up the composition and removes it.
Your insurance company is Google Insurance (it's the future...Google ran out of social media and content delivery ideas to try for a while and suddenly abandon so they had to branch out to find new areas to try and then abandon). Google's insurance AI won't approve the claim because the photo shows no damage, and it is Google so you can't reach a human to help.
> Reality is what we remember it to be. We can have a better reality easily ;)
Cue Paris Syndrome, because expectations will also be of a better reality. Then you go somewhere, and eat something, and experience the mess that actually exists everywhere before some AI removed it from the record.
This is just a progression of the nature of our human world - we have been replacing reality with the hyperreal for millennia, and the pace only accelerates. The map is the territory. Korzybski was right, but Baudrillard even more so.
It's sorta like this already, in the _present_ - people post photos with filters all the time, smart phone cameras color-correct and sharpen everything with AI (not just Samsung's). It'll just become more and more commonplace
The problem is that this particular AI enhancement was not advertised as such. Also, in the linked article it was putting moon texture on ping pong balls, which seemed like overzealous application of AI. Samsung could have marketed it as "moon enhancement AI" or something like that, which would be more honest.
My worry about these features becoming commonplace is that if everyone just leave those features enabled, we would end up with many boring photos because they all look similar to each other. The current set of photo filters, even though they seem to be converging on particular looks, at least don't seem to invent as much detail as pasting a moon that's not there.
I don’t understand why “AI” is even required for any of this, other than classification. Once classified as “the moon” the GPS and time, from the phone, could be used for a lookup table to a 10 gigapixel rendering, at the correct orientation, using NASA scans. It seems like a moon AI would give much worse results.
"Good sensors are expensive"-fun-fact: Mid-range CCTV cameras often have bigger sensors (1/1.8" or 1/1.2") and much faster lenses than an iPhone 13 Pro Max (1/1.9" for the main camera). The CCTV camera package is of course far bigger though. But still kinda funny in a way.
Edit: And the lenses on these are not your granddads computar 3-8/1.0, either. Most of the CCTV footage we see just comes from old, sometimes even analog, and lowest-bidder installations.
Bruce Sterling I think had a story in that direction. A polaroid camera producer would develop photos which would've been algorithmically enhanced so that their clients consider themselves better photographers and their cameras superior. I'm regularly updated for it for the last few years when cameras are more and more their software.
Edit: fixed the author's name. Cannot find the exact story though.
This has in some ways been happening for decades. There are a few countries where the way to take a good portrait of a person is to over expose the photo, so skin tones are lighter. People bought the cameras and phones that did this by default (by accident or design in the 'portrait mode' settings). They didn't want realism.
I still argue that my Galaxy Note 8 took cleaner pictures in general than my Galaxy Note 20. Everything feels overly processed, even in "pro" mode with all processing settings turned off.
I wonder if that'll ever cause legal problems in the future. Sorry, that photo someone took where the accused was in background at a party some years ago? He was kinda blurry and those facial features have been enhanced with AI, that evidence will have to be thrown out. Or maybe the photo is of you, and you need it as an alibi..
This is actually exactly what happened during the Kyle Rittenhouse case. A lawyer for the defense tried to question video evidence because of AI being used to enhance zoomed shots.
No that was what the mainstream media lied to you about what happened in the rittenhouse case. One of several instances where one could see fake news and straight up lies be spread in real time.
What actually happened was that a police officer testified that using pinch to zoom on his iPhone he saw kyle point his rifle at the first person assaulting him. Mind you we were talking about a cluster of around 5px. The state wanted to use an iPad to show the jury using pinch to zoom that same "evidence" because using proper zooming without an unknown interpolator algorithm the defense using an expert witness showed that this was not the case. No one in that courtroom understood the difference between linear and bicubic interpolation.
The defense did not understand it either so they tried to explain to the judge that the iPad Might use AI to interpolate pixels that aren't there and that the jury should only use the properly scaled video the court provided not an ad hoc pinch to zoom version in an iPad with unknown interpolation.
Thankfully the judge told the state to fuck off with their iPad but the mainstream media used the bad explanation of the defense against kyle when the reality was that the state basically tried to fake evidence live on stream using an iPad to zoom in.
BTW I'm German so I don't have a horse in the political race but I watched the Trial on live stream and saw the fake news come out while watching
The story isn’t “the state tried to fake” but rather the defense tried to get thrown out any image taken by a non analog camera as AI could have added detail where there is none.
I was watching this live for several days. That is not what happened. The defense paid an expert witness to upscale and enhance DIGITAL FOOTAGE using court appropriate tools. The defense never "tried to throw out any image taken by a non analog camera". They themselves USED DIGITAL FOOTAGE for their defense. I am sorry but you have been lied to by the media.
It was accurate in the sense that the lawyer used the word ai. In context the lawyer said "Apple uses logarithms (sic) and AI to enhance the images. I don't know how that works". The word AI was used but in context the non-technical lawyer simply meant image enhancing algorithms. No one in that room actually discussed AI.
That being said, one could interpret the top comment that way.
It’s funny, twenty years ago I was specifically told not to use digital cameras but the crappy disposable film camera provided in case of an accident precisely because the digital version could be contested in court.
That isn't far from how iphones work now. They have mediocre cameras, people only think they are good because they throw a lot of AI image enhancement at it.
Is Apple’s AI adding hallucinated details? The last I read it’s just used to merge multiple images - up to 8 or 9 images - to form the final
image. While I could see details getting lost or artifacts being added, I don’t think it can add actual “feature” details that don’t exist.
It already does some amount of features detection and targetted enhancing. Things like making this face smoother, or this sky bluer, or this grass greener, etc. From there, I wouldn't be too surprised if some details get added beyond what was strictly captured (e.g. bubbles in drinks, leaves in trees, …)
I wonder when the AI will hallucinate a gun into a black persons hand since the training black people often had guns? Hands moving fast are really blurry, so it has to hallucinate a lot, so it doesn't seem impossible. I could see that becoming a scandal of the century.
You (partly) joke but I do recall a recent shooting trial in which there was a lot of arguments about an "enhanced" or zoomed image and what was really in it.
I was thinking of a scenario. My children are adults and browsing photos of themselves as children. They come across a picture of the family on a vacation to the beach. They dimly remember it, but the memories are fond. They notice they are holding crisp ice cold cans of Coca Cola Classic (tm). They don’t remember that part very well. Mom and dad rarely let them drink Coke. Maybe it was a special occasion. You know what, maybe it would be fun to pick up some Coke to share with their kids!
So a future where reality and history are subtly tweaked to the specifications of those willing to pay…
These scenarios were much talked about a decade back in relation to advertising on photographs on Facebook, specially with Coca Cola and other popular brands.
Google already scans your photos folder and offers enhancements, stitches together panoramas and so on. So inserting product placement is totally believable.
I don't already fully trust the images, audio and videos I take with the phone.
I'm working close to HW and I actively use the camera/picture and videos for future reference and debugging. It's small, fits in your pocket, and the bloody thing can record at 240fps to booth!
Until you realize there's so much post-processing done on the images, video and audio you can't really trust and can't really know if you can turn it all off. The reality is that if you could, you'd realize there's no free lunch. It's a small sensor, and while we had huge improvements in sensor and small lenses, it's still a small sensor.
Did the smoothing/compression remove details? Did the multi-shot remove or add motion artifacts you wanted to see? Has noise-cancelling removed or altered frequencies? Is the high-frame rate real, interpolated, or anything inbetween depending on light just to make it look nice?
In the end, they're consumer devices. "Does it look good -> yes" is what thrums everything in this market. Expect the worst.
> Did the smoothing/compression remove details? Did the multi-shot remove or add motion artifacts you wanted to see? Has noise-cancelling removed or altered frequencies? Is the high-frame rate real, interpolated, or anything inbetween depending on light just to make it look nice?
This has been true of consumer digital cameras for 25 years. It's not new to or exclusive to smartphone cameras. It's not even exclusive to consumer cameras as professional ones costing many times more also do a bunch of image processing before anything is committed to disk.
Granted, there isn't the ridiculous amount of postprocessing we see in phones, but even many dedicated cameras these days don't give you an actual raw sensor dump in their so-called RAW files.
Sony is probably the worst offender here actually. The a6x00 series applies lossy (!) compression to every RAW file without any option to disable, and there's an additional noise filter on long exposures that wreaks havoc on astrophotography:
That's why I used the a6000 as an example. Even that is significantly better than what phones we're seeing today, where even the "RAW" is entirely artificial
A bit offtopic, but — a5100 is even cheaper, more compact (!) and has exact same imaging hardware, just with a slight artificial fps limitation. I've long ago upgraded to a6400 and still use the a5100 all the time when I don't plan any serious shooting.
I don’t know about android, but at least with my iPhone I’m pretty sure there are apps that can capture raw sensor data. Additionally I do have the ability capture Apple ProRAW format at of the photos. I don’t actually know if these images are still processed though.
Raw format? That means without any debayering applied? That would mean every pixel has only either r, g or b information and not combined. Be aware that there exist different debayering algorithms, sometimes applied depending on the context.
Also, without any correction for sensor calibration? That would mean every sensor has a different raw image for the same input.
My point being, without application of algorithms, the info captured by the sensor does not really look like an image.
This is my exact worry with things like chat gpt polluting the scrapable internet. The feedback loop might eventually ruin whatever value the models currently have by filling them with incorrect but plentiful generated nonsense.
"You just took a picture of the Eiffel Tower. We searched our database and found 2.4 million public pictures taken from the same location and time of day. Here are 30,000 photos that are identical to yours, except better. Would you like to delete yours and use one of them instead?"
'cause, yeah, what the world needs is YASIS, yet another stock imagery something. i'm way too outside the SV bubble to be affected by its reality distortion field to think that would be a good idea.
I find myself sometimes wondering with things like Peleton and Juicero, what would happen if Ron Popeil was born a couple decades later so he had access to the same VC those companies did?
I think they'll just use AI/GPT Hype and sell whatever next hypegrowth based on flimsy evidence they can.
The current narrative in financial places seems to be 2023 is gone but 2024 comes with a vengeance, feel safe investing, your real estate will grow in value! We're making people go back to the office so that it does! More lemmings! Less quality! More production!
Yes. I'm peak cynical after 3 years of self made economic destruction only to tell those who didn't cause it that "of course they must pay for the broken plates".
Misplaced ire? Maybe not well communicated in a brief spell between shopping for stuff on a Saturday.
Also, everytime someone invests a lot in explaining themselves in HN, if it goes against the current thread narrative, it'll still be ignored so it's not like it's particularly worth it. HN feels more Reddit and less Slashdot of old.
Still a nice aggregator to find stuff that might peak interest.
so is pretty much every famous building you can imagine someone wanting to specifically photograph. Eiffel's little tower isn't unique in this regard. Chrysler's little building in New York along with pretty much every other famous building is as well. You just have to hope that your framing with these structures is not considered the focal point of your image.
There was a pretty neat Google project a few years back that showed time-lapse videos of buildings under construction created entirely through publicly posted images that people had happened to take at the same spot over time.
You got a friend, spouse or someone close that has hundreds of pictures of you on their phone. Their phone has a "AI chip" that is used to finetune the recognition models and photo models with your AI library. Like Google Photos tags images of people you know, so does the model. It also helps sharpen images - you moved your head in an image and it was a bit blurry, but the model just fixed it, because like the original model had for the moon, it has hundreds of pictures of you to compensate.
One day, that person witnesses a robbery. They try and take a photo of the robber, but the algorithm determines it was you on the photo and fixes it up to apply your face. Congratulations, you are now a robber.
It seems fairly easy to bake a chain of custody into your images. Sensor outputs a signed raw image, AI outputs a different signed “touched up” image. We can afford to keep both in this hypothetical future; use whichever one you want.
Once generative AI really takes off we will need some system for unambiguously proving where an image/video came from; the solution is quite obvious in this case and many have sketched it already.
And the answer is “spam filters and AI personal curation agents will drop any image without chain of custody from every feed that claims to be about reality”.
In a world where any image or video can be generated, chain of custody to a real-world ground-truth will be vitally important.
I think anything analog is going to be suspect; you can take a photo of a digital image and it could look like a real analog image.
Absent a chain of custody (perhaps including GPS baked into the signed image data blob), I think analog artifacts will become untrustable. Unless you can physically date them to pre-generative era!
So now not only are there AI-imagined details in your images but those details are also different depending on which device the image is viewed on. Lovely.
The images generated by SLR and mirrorless cameras are already signed with device embedded keys during EXIF embedding. Every manufacturer sells such verification systems to law enforcement or other institutions to verify such images.
Sometimes there are exploits which extract these keys from the cameras themselves, but I don't hear them nowadays.
It’s a fair point but with high enough resolution (and perhaps GPS baked into the trusted data) I suspect it would be very hard to actually forge a digital image from an analog source.
Likewise depth fields and other potential forms of sensor augmentation.
For the long time digital cameras embedded in EXIF metadata about conditions on which the photo was made. Like camera model, focal length, exposure time etc
Nowadays this metadata should be extended with description of AI postprocessing operations.
> Nowadays this metadata should be extended with description of AI postprocessing operations.
Of course. But to ensure that's valid for multiple purposes we need a secure boot chain, and the infrastructure for it.
To get there we need an AI arms race. People trying to detect AI art with machine learning vs. increasing AI sophistication. Companies trying to discourage AI leaks of company secrets and reduce liability (and reduce the tragic cost of mistakes of course) vs. employees being human.
Or we could have built a responsible and reasonable government that can debate and implement that.
Maybe I'm naive. I'll take responsibility for that.
In the meantime, it's playtime for the AIs. Bring your fucking poo bags, theyre shitting everywhere (1), pack it in, pack it out.
(1) what the world didnt know, was that this was beautiful too.
> Of course. But to ensure that's valid for multiple purposes we need a secure boot chain, and the infrastructure for it.
>
> To get there we need an AI arms race. People trying to detect AI art with machine learning vs. increasing AI sophistication.
Or we can just recognize the lunacy of it and opt out of caring. You can't stop the flood, so you just learn to live with it. With the right view, the flood becomes unimportant.
Secure boot in practice always become slave boot. The user loses control to even control the operating system running on his device. It is the final nail in the coffin for the already dying concept of general purpose computing.
What measures can the government implement to combat this? AI image modification is realistically possible even on consumer hardware running locally. There is no going back.
Obviously someone who has good enough position to take semi-clear photo and who knows you so well, that has phone full of your face, will not recognize you directly, but will be convinced that you are robber after looking at photo. At this point we can go full HN and assume that you will be convinced anyway, because judge is GPT-based bot.
This "future" is present in current Pixel lineup btw. Photos are tagged as unblured, so for now you can still safely take a selfie with your friends.
Photos taken by cell phone cameras increasingly can't be trusted as evidence of the state of something. Let's say you take a picture of a car that just hit a pedestrian and is driving away.
Pre-AI, your picture might be a bit blurry, but say, it's discernible that one of the headlights had a chunk taken out of it; it's only a few pixels, but there's obviously some damage, like a hole from a rock or a pellet gun. Police find a suspect, see the car, note damage to the headlight that looks very close, get a warrant for records from the suspect, find incriminating texts or whatnot, and boom, person goes to jail for killing someone (assuming this isn't the US, where people almost never go to jail for assault, manslaughter, or homicide with a car) because the judge or jury are shown photos from the scene, taken by detectives in the street of the person's driveway, and then from evidence techs nice and close-up.
Post-"AI" bullshit, the AI sees what looks like a car headlight, assumes the few-pixels damage is dust on the sensor/lens or noise, and "fixes" the image, removing it and turning it into a perfect-looking headlight.
Or, how about the inverse? A defense attorney can now argue that a cell phone camera photo can't be relied upon as evidence because of all the manipulation that goes on. That backpack in a photo someone takes as a mugger runs away? Maybe the phone's algorithm thought a glint of light was a logo and extrapolated it into the shape of a popular athletic brand's logo.
I’d just like them to fix the problem where license plates are completely unreadable by most consumer cameras at night. It’s almost as though they are intentionally bad. (The plate ends up as a blown out white rectangle.)
However, their omnipresent surveillance data will show that eight years, seven months, and thirteen days earlier you cut off the DA's third cousin while driving on the freeway, so the DA will conveniently forget to present this alibi as evidence.
AI isn't the thing to be worried about. People with power abusing AI is the thing to be worried about.
One day, that person witnesses a robbery. They try and take a photo of the robber, but the algorithm determines it was you on the photo and fixes it up to apply your face. Congratulations, you are now a robber.
Sounds like pretty standard forensic science, like bite marks and fingerprints.
I feel like with ScarJo lately you either get "big budget movie where she's phoning it in" or "small movie most people won't watch where she's a great actress. Does this fall into either of those categories?
AI-based image generation is surely already good enough that a single digital photo can't count as evidence alone. But your scenario doesn't make much sense to me - are you suggesting AI will have reached a point it's stored and trained on images of almost everyone's faces, to the point it could accurately/undetectably substitute a blurry face with the detailed version of an actual individual's face it happens to think is similar?
I'd be far more worried about deliberate attempts to construct fake evidence - it seems inevitable that eventually we'll have technology to cheaply construct high-quality video and audio media that by current standards of evidence could incriminate almost anyone the framer wanted to.
Look similar to a celebrity? Your face gets replaced, because the number of photos of the celebrity in the corpus outweighs photos of you. And when those doctored photos end up in the corpus, weighting will be even further towards the celebrity So people who look less like the celebrity get replaced, because it is almost certainly them according to the AI. Feeding back until everyone gets replaced by a celebrity face. And then the popular celebrities faces start replacing the less well known celebrities. And we end up with true anonymity, with everyone's face being replaced by John Malkovich.
The recent kyle rittenhouse trial had an element that hinged on whether apple's current image upscaling algorithm uses AI, and hence whether what you could see in the picture was at all reliable. The court system is already aware of and capable of dealing with these eventualities.
“Aware of” does not necessarily mean “capable of dealing with”. Forensics is generally bad science, yet gets admitted into court all the time. This occurs despite many legal textbooks, papers, and court opinions highlighting the deficiencies.
The question was more general, if the iPad zooming introduced any different pixels (e.g. a purple pixel between red and blue). Or, "uncharged pickles" as the judge put it.
It doesn't even need AI to be problematic. Pinch-zoom has no business being used in the courtroom as it inherently can introduce issues. However, a fixed integer ratio blowup of the image shouldn't be problematic. (2:1 is fine. 1.9:1 inherently can't guarantee it doesn't introduce artifacts.)
Whether zooming in on an image on iPad adds "extra" details was already a contentious discussion during Kyle Rittenhouse trial. The judge ultimately threw that particular piece of evidence out, as the prosecution could not prove that zooming in does not alter the image.
imagine you want to "scan" a document using camera app like many people do, and ai sees blurry numbers and fixes then for you.
when will you notice that some numbers even that look clear are different than on original document?
I think this is so we'll known case that Samsung wouldn't make such trivial mistake and if ai detects you are photographing a document it'll disable ai magic automatically, but imagine scenario eg. where you are trying to photograph some phone number from some banner, would ai also guess it shouldn't mess with numbers there? imagine someone with poor eyesight wants to read this way something from a sign that is far away
and I would bet that super zoom would be attractive feature for person with such sight issues
I thought it was really funny in the 1980s that people in medical imaging were really afraid to introduce image compression like JPEG because the artifacts might affect the interpretation of images but today I see article after article about neural image enhancement and it seems almost no concern that a system like that would be great at hallucinating both normal tissue and tumors.
So far as law and justice goes it is the other way around too. If it is known to be possible that cameras can hallucinate your identity, it won't be possible to use photographic proof to hold people to account.
Something I learned long ago is that people typically don't want the truth, in general. They want fictional lies, they crave a false reality that makes them happy. Reality in and of itself, for most, is an utter drag if they're made constantly aware of it and dwell on it. When it comes to marketing, people eat up the propoganda techniques. They want to be fed this amazing thing even if it's not really all too amazing. They love that it tickles their reward center in the process.
This of course isn't always the case. When something is really important or significant people sometimes do want to know the truth as best they can. I want to know the car I'm purchasing isn't a lemon, I want to know the home I'm buying isn't a money pit, I want the doctor to to tell me if my health is good or bad (for some, under the condition the information is actionable), and so on.
When it comes to more frivolous things, for many, build the fantasy, sell them that farm to table meal you harvested from the dew drops this morning and hand cooked with the story of your suffering to Michelin star chef and how you're saving my local community by homing puppies from the local animal shelter with profits... even if you took something frozen, slapped it in the microwave and plated it and just donate $10 a month to your local animal shelter where you visited twice to create a pool of photos to market. For many, they want and crave the fantasy.
Progress made by science and tech has, for a brief fragment of history, established techniques and made practical, in some cases, to peel away all or at least some layers of fantasy away to reality. We started to pierce into cold hard reality and separate the signal of truth, as we can best understand it, from all the noise of ignorance and fantasy.
For many fantasy lovers, snakeoil salesmen, and con men, pulling away the veil of fantasy and noise has been a threat and there's been a consistent battle to undermine those efforts. The whole emergence and perpetuation of misinformation and recent "fake news" trends are just some of the latest popular approaches. We've been seeding our knowledge and information more recently with increasing degrees of falsehoods and pure fabrications.
Now, enter "AI," especially generative flavors. The same people who wanted to undermine truth are foaming at the mouth at the current ability to produce vast amounts of noise that in some cases are almost indistinguishable from reality from current techniques we have. Not only that, fantasy lovers en masse are excited at the new level of fantasy they can be sold. They really really don't care or want the truth. They really do just want "a good looking picture", "to make the summary interesting", or just see some neat picture. They don't care how accurate it is. Now people interested in the truth are facing a deluge of technologically enabled difficult to seperate noise production.
Is what I'm looking at close to reality? How many layers of noise are there I should consider when interpreting this piece of information? In the past, the layers used to be pretty managable, they were largely physical limitations or resource limitations to falsify the data to a point that couldn't be easily discerned. These days... it's becoming increasingly difficult to determine this and more and more information in various forms are leveraging more sophisticated and believable noise production. Technology has made this affordable to the masses and there are many parties with interest in setting the clock back to a world where the best story tellers are looked at as the oracles of modern time.
People often scoff at ChatGPT that it seeds or "hallucinates" to interpolate and extrapolate gaps of knowledge and make connections but it does so in a way that people like. It projects confidence, certainty, and in many cases it gives exactly what people want. To me, it's scary because it's providing a ...
Eventually people won’t care much for clarity and precision, that’s boring. The real problem is that everything that can be photographed will eventually have been photographed in all kinds of ways. What people really want is just pictures that look more awesome, in ways other people haven’t seen before.
So instead, raw photos will be little more than prompts that get fed to an AI that “reimagines” the image to be wildly different, using impossible or impractical perspectives, lighting, deleted crowds, anything you can imagine, even fantasy elements like massive planets in the sky or strange critters scurrying about.
And thus, cameras will be more like having your own personal painter in your pocket, painting impressions of places you visit, making them far more interesting than you remember and delighting your followers with unique content. Worlds of pure imagination.
We already have people demanding higher definition televisions to watch AI-sharpened 4K restorations of old films whose grain and focus would annihilate any details that small worth seeing.
There's an arms race between people adding nonexistent details to old films and people manufacturing televisions with dense enough pixels to render those microscopic fictions. Then they lay a filter over it all and everything becomes smooth gradients with perfectly sharp edges.
What you're imagining is Hyperreality from Simulacra and Simulation and has been happening since the invention of the television, and later the internet.
AI will accelerate this process exponentially and just like in The Matrix, most people will eventually prefer the simulation to reality itself.
People want this. It's already happening. There was a post on the Stable Diffusion reddit where someone ran a picture of their grandparents through it to colorize and make it "look nicer". But it made significant changes to their clothes and hallucinated some jewelry they weren't wearing, along with some subtle changes in the faces. It's not real anymore, but hey it looks nicer right?
Oh man... I thought you were going to, "the stop signs and strip malls were how we discovered there were aliens on the Moon (and Mars) that look exactly like us!".
Of course they would have perfect skin and expertly applied eye-liner and lipstick as well.
The novelty of things like instagram is wearing off. I see more people not bothering to pull out their phone. It's not just wanting to compete with the photos taken by narcissist on the internet, it's also just losing interest, and knowing things you share can and will be used against you.
Or, you know, Samsung sells ad placements in the enhanced images to do things like turn a can of Coke into a can of Pepsi, overwrite billboards in the background, etc.
Quite the equivalent (to me) to many kids preferring the taste of "strawberry yoghurt" compared to real strawberries, because it's sweeter and has enhanced taste. Except for photos.
This will be the next step in film “restorations” too.
A combination of ai models trained on high resolution textures and objects, models of the actors, and training from every frame of the movie that cal use the textures and geometry from multiple angles and cameras to “reconstruct” lost detail.
I've seen this with CGI. CGI still looks awful somehow, but people about my age think it looks cinematic, and people a decade younger think it looks incredibly realistic.
I’ve always thought this was the final outcome of all AI; a feedback loop. Same when ChatGPT starts using things ChatGPT wrote itself as references to train itself.
I guess there is some AI algorithm that does zoom as postprocessing. That AI knows the moon so it can fill in the blanks and compensate for a (relatively) crappy sensor.
So in the future, there are either cameras that can see what others have seen before, and those that can truly capture new, true, detail (true as in, without filling it with estimations)
The Gaussian blur they applied is theoretically reversible in a continuous function and infinite precision. In a discrete function (like a image in the computer) and only a few dozens of bits it's not 100% reversible, but it can be partially undone and get a sharper image (that is not as sharp as the initial image).
I think they're probably right about the AI-sharpening using specific knowledge about the moon... However, they are wrong about the detail being gone in the gaussian-blurred image.
If they applied a perfect digital gaussian-blur, then that is reversible (except at the edges of the image, which are black in this case anyway). You still lose some detail due to rounding errors, but not nearly as much as you might expect.
A gaussian blur (and several other kinds of blur) are a convolution of the image with a specific blur function. A convolution is equivalent to simply multiplying pointwise the two functions in frequency space. As long as you know the blur function exactly, you can divide the final image by the gaussian function in frequency space and get the original image back (modulo rounding errors).
It is not totally inconceivable that the AI model could have learned to do this deconvolution with the Gaussian blur function, in order to recover more detail from the image.
While the information might be recoverable, the information is not seen by the camera sensor. Hence I think the argument in the post stands. Some AI model/overlay magic is happening, pretending to display information the sensor simply did not receive.
But the AI should not have learned to apply a Gaussian deconvolution kernel. If anything it should be applying a lens-based bokeh kernel instead. A true lens blur does not behave like a Gaussian blur.
You certainly wouldn't be able to recover any detail this way (in fact, deconvoluting Gauss after taking a photo of the picture displayed on a computer screen won't give you any kind of sensible results either - try it yourself).
> As long as you know the blur function exactly, you can divide the final image by the gaussian function in frequency space and get the original image back (modulo rounding errors).
Those rounding errors are very important though. The Gaussian function goes to zero very quickly and dividing by small numbers is not a good idea.
If your deconvolving a noise free version of the original that also doesn't have any saturated pixels (in the black or white direction) then you can get the pretty close to the original back. I don't think this applies here because the OP is taking a picture of a screen that shows the blurred version, so we've got all kind of error sources. I think the OP is right: the camera is subbing in a known picture of the moon.
It would be interesting to see what happens with anisotropic blur for example, or with a picture of the moon with added fake details (words maybe?) and then blurred.
Author tested for this by doing the experiment again with detail clipped into highlights, completely gone, model detail was added back.
> To further drive home my point, I blurred the moon even further and clipped the highlights, which means the area which is above 216 in brightness gets clipped to pure white - there's no detail there, just a white blob - https://imgur.com/9XMgt06
> I zoomed in on the monitor showing that image and, guess what, again you see slapped on detail, even in the parts I explicitly clipped (made completely 100% white): https://imgur.com/9kichAp
While I think this is a great test, I'm not really sure what that second picture is supposed to be showing. Kinda seems like they used the wrong picture entirely.
Ah! Thank you! I wasn't getting the controls for some reason.
Given how small the pure-white areas are, tbh I'm not sure I'd consider that as having "added detail". It has texture that matches the rest of the moon, but that's about as far as I'd be comfortable claiming... and that seems fine, basically an un-blurring artifact like you see in tons of sharpening algorithms.
I do think this "clip the data, look for impossible details" is a very good experiment and one that seems likely to bear fruit, since it's something cameras "expect" to encounter. I just don't think this instance is all that convincing.
---
And to be clear, I absolutely believe Samsung is faking it, and hiding behind marketing jargon. The outputs are pretty ridiculous. They may not be "photoshopping on a texture", but training an AI on moon pictures and asking it to add those details to images is... well, the same thing Photoshop has features for. It makes no difference - it's not maximizing the data available, it's injecting external data, and they deserve to be slammed for that.
I watched the video and in this case the "recovered" detail is clearly natural to me. The original case does look like some kind of moon-specific processing, but this one with clipped highlights seems natural and can be achieved using classical CV.
> However, they are wrong about the detail being gone in the gaussian-blurred image.
Well yes, but he also downsampled the image to 170x170. As far as I know, downsampled information is strictly lost, and unrecoverable without an external information source (like an AI model trained with pictures of moon).
I'm too lazy to downscale it myself, so here's a 180x180 picture of the moon from WP [1]. This looks about the same as the Samsung result [2]. They are not getting the original detail, but they are getting the detail they should expect if Samsung simply deconvolved the blurred image.
This is wrong. The blurred image contains only intensity information, but reversing the convolution in frequency space would require phase information as well. A simple Gaussian blur is not reversible, even in principle.
There is no "phase information" in the spatial domain. "Phase" is literally, where the pixels are on the screen.
Rather, reversing blur of any type is limited (a) by spatial decimation (a.k.a. down sampling, which is performed in the article), and (b) by noise/quantization floor, below which high frequency content has been pushed.
The input of the DFT is real, but the output is complex. Filtering in the Fourier domain means that the DFT of the image is multiplied with that of the filter. The resulting complex array is than converted into an output image by taking the magnitude. This destroys the phase information and makes the operation irreversible.
Another point of view is that there are infinitely many images that will produce the same result after blurring. Obviously, this makes the operation irreversible.
Both an image and a convolution have a conjugate-symmetric DFT, and the phase of each complex bin encodes spatial information, and there's no "taking the magnitude" involved anywhere when turning a spectrum back into an image (only complex cancellation).
> The resulting complex array is than converted into an output image by taking the magnitude.
No, it's emphatically not. Perhaps you are thinking of displaying a spectrogram.
To produce an image from frequency-domain data, inverse DFT must be applied. Since (as @nyanpasu64 points out), the DFT of a real-valued image or kernel is conjugate-symmetric (and vice-versa), the result is again real-valued without loss of information. The phase information is not lost. If it were, the image would be a jumbled mess.
(Not that DFT+inverse DFT is necessary for Gaussian blur anyway -- you simply convolve with a truncated Gaussian kernel.)
> Another point of view is that there are infinitely many images that will produce the same result after blurring.
No, this is not true. I don't know why you think it is. This is only true of a brick wall filter, which Gaussian filter is not [1].
The SNR of high-spatial-frequency components is reduced for sure, which can lead to irrevocable information loss. But this is nothing to do with phase.
That is mathematically true but not practically. Though indeed the Gaussian kernel has lots of zeros [1], in actuality, (a) the zeros themselves are at points, not regions, and therefore of little consequence, and (b) in practice the noise generated from reamplifying frequencies near these zeros can be minimized via techniques such as Wiener deconvolution [2].
> If they applied a perfect digital gaussian-blur, then that is reversible
Actually any noise distribution is frequently reversible if you know the parameters and number of steps. This is in fact how diffusion models work (there's even work of Normalizing Flows removing realistic camera noise). It is just almost impossible to figure this out since there are many equivalent looking ways. But we need to be clear that there is a difference between reversibility and invertibility. A invertible process is bijective, or perfectly recreates the original setting. A reversible process can just work in both directions and isn't guaranteed to be invertible. (Invertible means reversible but reversible doesn't mean invertible)[0]
I bring this up because even more complicated versions of bluring could be argued as not "faked" but rather "enhanced." A better way to test Samsung faking the data is to mask out regions. If the phone fills in the gaps then it is definitely generating new data. This can still be fine if the regions are small, unless we also want to call bilinear interpolation "faked" but I don't think most people would. This is why it gets quite difficult to actually prove Samsung is faking the image. I don't have a Samsung phone to test this though.
So basically I'm with you, and even a slightly stronger version of this
> It is not totally inconceivable that the AI model could have learned to do this deconvolution with the Gaussian blur function, in order to recover more detail from the image.
Edit: After reading other comments I wanted to bring some things up.
- The down scaling is reversible, but not invertible. We can upscale, reversing the process. But yes, there is information lost. But some data can still be approximated and/or inferred.
- The clipping experiment isn't that good. Honestly, looking at the two my brain fills in the pieces and they look reasonable to me too. Clipping the brightness isn't enough, especially since it is a small portion of the actual distribution. I did this on both the full image and small image and both are difficult to distinguish by eye from the non-clipped. Clipping below 200 seems to better wash out the bottom of the moon and remove that detail. 180 seems better though tbh.
The level of BS in this thread perfectly resembles the BS in religious-level audiophile discussions. A mixture of provably correct and provably incorrect statements all mixed together with common words used in uncommon ways.
> But yes, there is information lost. But some data can still be approximated and/or inferred.
Not all information is equally important though. Most people can't tell flac from a high quality lossy compression. If you cut off everything above 19khz and below 70hz you've lost information but not important information. The same analogy holds true about imagery. Which information is lost is important which is why I discuss clipping different levels and masking to make a stronger case. I'm just saying I don't think we can conclude an answer from this experiment, not that their claim is wrong. I want to be clear about that.
A major problem with blur beyond rounding errors, say due to the optics being somewhat blurry due to manufacturing difficulties and tradeoffs for weakening assembly tolerance requirements (like wanting rotationally symmetrical optical surfaces, despite a rectangular shaped actively-used image focal plane (e.g. CMOS photodiode array), and potential for specializing the design to evenly light up _just_ that rectangle), is that the photon shot noise has a standard deviation equal to the square root of the photon count.
A smartphone sensor pixel has space for some low 4 digits number of electrons (created with some probability from photons, but that stochastic effect doesn't matter for anything a normal user would photograph) and typically should have a fixed 2~10 electron standard deviation from the analog-to-digital-converter (well, mostly the amplifiers involved in that process).
So if your pixel is fully exposed at a high 10000 electrons, and you √ that, you have 100 electrons stddev from shot noise plus worst case 10 electrons stddev from the readout amplifier/ADC.
If you have a dark pixel that only got 100x less light to only have accumulated 100 electrons, √ of that gives 10 electrons stddev of shot noise plus the same 10 electrons stddev readout amplifier/ADC.
The problem is that while you have an SNR of 5 with the dark pixel, when trying to deconvolve it out of a nearby bright pixel, even perfectly with no rounding errors (1 electron = 1 ulp/lsb in a linear raw format), you now have 100/110 = 10/11 ≈ 0.91.
That's far worse than the 5 from before.
This gets worse if your ADC has only the 2 electrons stddev instead of the 10 (about 2x worse here).
That's the reason why deconvolution after the photon detector is a band aid that you only begrudgingly tend to accept.
The trade-off just requires massively increased aperture/light gathering, likely negating your savings on optics.
>If they applied a perfect digital gaussian-blur, then that is reversible
Not true. Deconvolution is a statistical estimate. Think about it. When you blur, colors get combined with their neighbors. Statistically this moves toward a middle grey. You're compressing the gamut of colors towards the middle, and thus losing information. Look at an extreme case - 2 pixels of mid-grey. It can be deconvoluted to itself, to a light and dark grey, or to one black and one white. All those deconvolutions are equally valid. There's no 1-to-1 inverse to a convolution. If you do a gaussian blur on a real photo and then a deconvolution algorithm you'll get a different image, with an arbitrary tuning, but probably biased towards max contrast in details and light noise, since that what people expect from such tools and what most real photos have. But, just like A.I. enhanced images, it's using statistics when filling in the missing data.
Wow, that is so cool, and such a good writeup. I like the analogy to an encrypted file, and the key being the exact convolution. The amount of information lost is the amount of information in the key.
I wonder if there is some algorithmic way to find the key and tell if it's correct - some dictionary attack, or some loss function that knows if it's close. Perhaps such a thing only works on images that are similar to a training set. It wouldn't work on black and white random dots, since there'd be no way for a computer to grade or know statistics for which deconvolution looks right.
Gaussian blur is essentially acting as a low pass filter. An IR filter does not strictly destroy information in the filtered spectrum components, but does attenuate their power.
Given a perfect blurred image, reconstruction is possible - however due to the attenuation, these high frequency components are ~sensitive~.
Apart from quantisation effects [you mentioned which limits perfect de-convolution], adding a little AW Gaussian noise(such as taking a photo of the image from across the room) after the kernel is applied obliterates high frequency features.
Recovery when noise is low (plus known glyphs) is why you should not use Gaussian blur followed by print screen to redact documents.
Inability to recover when there are artifacts and noise is [part of] why cameras cannot just set a fixed focus [at whatever distance] and deconvolve with the aperture [estimated width at each pixel] to deblur everything that was out of focus.
TLDR for readers, It is unlikely to recover sufficient detail via de-convolution here.
While I understand what the author tries to say, I have to point out that ship has long sailed. Samsung just pushed it a bit too far and slapped a "scene optimizer" label on it.
AI has been used in "cell phone photography" for a few years, at lease since Pixel 2 where a mediocre sensor produced much better pictures than what people expect (maybe there are other players who did this even earlier). And every manufacturer started doing it, including Apple. Otherwise, do you think "night mode" is just pure magic? Of course not, algorithms are used everywhere.
How do you define "fake"? In podcasts, Verge editor Nilay Patel has asked various people "what is a photo", because the concept of a "photo" has become increasingly blurry. That is the question the author is asking, and people may have different answers from the author's.
Photo is an interesting word. It's meaning is clarified by other words, such as photorealism, photofinish. These words will (strictly) lose their meaning if photograph simply means image captured and processed by a device.
Curiously and revealingly, the political word photo-op stands alone in this photo- parade of words in the age of photo-imaginings. The universe does indeed have a sense of humor.
> Otherwise, do you think "night mode" is just pure magic?
Night mode definitely uses some AI but most of the result is from stacking frames. Samsung here did not label it as a "scene optimizer". Their marketing just calls it Space Zoom. The only disclaimer they provide is "Space Zoom includes digital zoom, which may cause some image deterioration."
According to Samsung -- and I just confirmed it on my S22 under Camera -> Camera Setting -- it's called "Scene Optimizer"[1]:
[ Overview of moon photography]
Since the Galaxy S10, AI technology has been applied to the camera so that users can take the best photos regardless of time and place.
To this end, we have developed the Scene Optimizer function, which helps AI to recognize the subject to be photographed and derive the optimal result.
From Galaxy S21, even when you take a picture of the moon, ai recognizes the target as the moon through learned data, and multi-frame synthesis and deep learning-based ai technology when shooting. The detail improvement engine function that makes the picture clearer has been applied.
Users who want photos as they are without AI technology can disable the optimum shooting function for each scene.
Using algorithms to take multiple pictures and stack them together is fine. The information is real, exists, and objective. People in the background won't (for example) suddenly be facing the other way because of the algorithm.
The problem is that AI isn't just interpolating data. It is wholesale adding extra data that simply doesn't exist. The person in the background is facing left, but the sensor couldn't possibly have captured that detail even after multiple images--it was a coin flip that the AI made.
The issue is that, like privacy, most people won't care ... until they do. By that time, it will be too late.
Note that this behaviour is limited to scene mode, which has a moon shoot mode. You can always use the normal or pro mode where the pictures are not magically enhanced.
Is is ridiculous that OP consider this "cheating". Most people just want a nice picture and don't give a damn about AI.
The software technology in the original pixel cameras were using multiple frames of varying exposure to allow for impressive dynamic range in images while still retaining colour and contrast. This is quite a difficult thing to do as requires precise understanding of what the 'edge' of an object is, and I think that is what AI was used for. This stacking technique is also used for night exposures.
I'm sure that they have started using AI to fill in details more recently, but this is just to point out clever use of multiple exposures and AI can help without faking detail.
> I downsized it to 170x170 pixels and applied a gaussian blur, so that all the detail is GONE. This means it's not recoverable, the information is just not there, it's digitally blurred
Strictly speaking, applying a Gaussian blur does not destroy the information. You can undo a Gaussian blur with a simple deconvolution, which is something I would expect even a non-AI image enhancement algorithm to do (given that, you know, lenses are involved here).
I'd like to see what detail can be "recovered" with just the downsizing, which DOES destroy information.
Well the op did downsize so details had to be reconstructed. Also the noise from having the image being projected through a screen and then a retaken through the camera sensor means that it isn't just your standard perfect convolution.
if you downsize an image to 170x170px and then blow it up so it's visible to a camera from across the room without any sort of blurring, it's not going to look like anything and the camera's object detection won't recognize it as the moon - it's just going to look like a huge pixel grid.
I am not quite so confident. I would like to see an experiment to test how badly you can distort an image of the moon before the AI stops recognising it.
I'm not so confident either, especially when you consider than whatever input the NN gets is probably downscaled to hell from however many gigapixels the sensor has, otherwise good luck running it on a phone with 8GB of ram.
In this regard, the AI is acting just like the human brain - adding details that may or not be present in what the viewer is seeing. Details that the viewer expects to see, so the brain delivers against this expectation.
A gaussian blur is not a good test. It does not technically remove all the information from the image. As such this test can't distinguish between unblur or actual moon-pasting.
Binning the image, or cropping it in Fourier space, would be a better test.
Has anyone found a decent use of the 100x zoom on these phones? Could it be that the sheer hype it causes compensates for the extra cost of putting it on the phone? Like, even if nobody uses it it stills turn an overall profit because of the marketing
Not sure about the current situation, but a couple years ago could receive tons of updoots on Reddit for posting a video how you zoom eg from one side of the harbour to see something on the other side.
This is just make hipsters get into old point and shoot digital cameras... good thing I kept my Canon A540.
All the subtle trickery manipulation that the smart phone's doing to reality is concerning. Smoothing people's faces, making their eyes pop, enhancing the shit out of the colours, and now plopping fake objects overtop of the real ones.
Future concerns of this technology should range from a low-key disconnect from reality, to the complete inability to photograph certain objects or locations.
Imagine dusting off a 30 year old digital camera, finding some AA batteries to put in it, snap a selfie and then realizing just how ugly we all are and how washed out the polluted world actually looks without a bunch of narcissism-pandering enhancements.
I regularly take photos of text etc because I am not going to remember it. If a photo of a config password is AI fucked into showing the wrong digits, there’s a real problem.
Their representation was consistent for one thing. For another it was well known that a) the world isn't black and white and b) b&w is just the tech we have right now. For a third thing, once color came out, black and white was still popular because it was a known art form that exposed characteristics of a scene which a color photograph might miss. So it was fudging images in a way that was predictable.
Compare this to AI/ML-manipulated photos that aren't labelled as such. In this case we no longer know if it's our brain fudging a picture or our camera, so we are starting to lose an arbiter of truth.
Side-rant but it'll be weird when this makes it into high-end cameras that save RAW images. Will RAW still be "raw?" I don't know enough to say.
I can highly recommend older Nikon DSLRs and older AF-D or manual focus AI-s lenses. Youbhet full frame bodies, the awesome D700 for example, and a very decent set of lenses rivaling the holly trinary for around the same price you pay for a Z50 with a kit lens. Nothing wrong with a Z50, it is a great camera. But the old, used gear is just such better value IMHO. Ai-s lenses are getting more expensive so since a couple of years so.
The remaining budget can go into going to nice places to shoot nice photos, and to print them nicely and large enough to put on your wall choice.
DSLRs are insane value right now as everyone jumps to mirrorless. Which is just silly fashion, IMHO. (Or, if you're like me and prefer optical viewfinders, kind of important). I'm jazzed that people are abandoning things like cds, physical books, and DSLRs - more for me. An aside: I can't believe people have so quickly gone from "geez should I even shop online" to "hey lets put my whole live online and burn the physical copies". That happened in the last 20 years. No engineer or risk analyst in the world could say this is a good idea. Computers, software, and the internet need to be stable for 100+ years to be relied on to that degree, IMHO.
If you start from scratch, used mirrorless bodies are good value as well. Lenses tend to be on rather expensive so. I consider myself lucky so, old screw drive AF Nikkor lenses don't auto-focus on mirrorless, and thus rather cheap atm. The old AI-s lenses on the other hand are manual focus anyway, and since they are optically really good, are becoming more expensive now.
canon is also a great choice because the ef mount is larger than most other dslr and it can fit pretty much every dslr lens that exists (vintage and new) without the need for an expense adapter that has optical elements. You can even 3d print them
Between Nikon and Canon, the choice comes down to taste and personal preferences. Both are equally good. Same goes for mirrorless between Nikon, Canon, Sony, Fujifilm or Panasonic. Whatever you pick, it is not wrong or a mistake.
I'm 40 and did the same thing, bought a cheap analog camera with black and white film to shoot fun photos on my birthday party :) The film is still at the lab, but I guess it's worth the wait.
I love my Canon AE-1 but dang it's expensive to actually use.
The G9x Mark II, Sony RX100, Panasonic LUMIX and similar 1" sensor cameras are awesome though and I don't think they've gotten too crazy with computational photography. I imagine some color processing modes might be doing a bit of work though.
I have a few questions: Is this specific for the moon or is the AI generally sharpening around dark areas? If it is specific to the moon then how many common objects does the camera recognize? If you did the same test with a picture of a stop sign or a popular car would the results be similar? Also is this processing done on the camera or does this require an internet connection and work on Samsungs end? I have known about AI image enhancement for years but the idea of recognizing then re-texturing common objects is something I had never considered.
It's creepy how deceptive phone cameras are. I've noticed in pictures that I've taken that certain elements have been postprocessed to look better than they actually are.
Orientation is because photos were taken months apart.
This is to get an idea of quality, with the APS-C camera having a much larger (26x area) sensor and better optics to work with.
My impression is... S21 Ultra "space zoom" is, at best, a good party trick. But if you zoom in, the quality is still nearly garbage. Not an objectively "great photo of the moon."
You realize that these photos are to be compared with other phones and not some apsc on tripod, thats a ridiculous premise for everybody understanding 101 of photography that not even Samsung during any release was claiming to beat.
It still shows you much more details than visible via eyes, so yeah its a party trick (what else would moon shot on phone be), but pretty darn great at that (I haven't seen so many people with :-O since iphone 1 release when showing this... then they quickly try their top iphones and xiaomis and end up consistently with a small white blob).
There is one aspect that this phone wins at easily - it can take that moonshot (TBH it can be a bit sharper than yours) while handheld, pretty consistently. Good luck trying that with your apsc with such a long shutter, it will consistently end up in just a blur. Software often beats raw hardware even these days.
I have a similar photo done on S22 ultra on March 6 this year, and neither look like your (position of lower right mega crater but also the rest). So its not simple 'photoshop-into-predefined-nice-image'.
I can clearly see that most folks here don't actually own discussed devices (which is fine, its US-based HN, a bastion of iphone and many Apple employees dwell here and uncritical appreciation of Apple is very evident in every single related thread). I've used its 10x zoom extensively over more than a year, it simply blows all other phones away easily for that kind of situation (more than those rather weak 3x zooms available everywhere). Family photos, wild animals, nature, anything you want to come closer, otherwise the scene is tiny dots in the center like on other phones. It works really well for what it is, with obvious unavoidable physical limits.
Overall this phone made me put my fullframe Nikon D750 away on a day I bought it. I took it 'just to be sure' on vacation to Egypt last year, didn't touch it a single time. Most often it doesn't produce strictly as good images but a) they are good enough to be viewed on phones side by side easily, basically as good as fullframe there and sometimes even much better, ie handheld photos in the night of dark scenes, fullframe is utterly lost without tripod, and b) it weights 0 and takes 0 extra space (and cost 0 instead of many thousands for modern camera with big sensor), since I have phone with me always anyway.
Tried exactly the steps as author of article, couldn't reproduce it a bit, tried various mega zooms, his various original photos, dark room etc. Blur remained blur, nothing added. I mean at this point everybody acknowledges any decent phone is painting quite a bit (ie iphone taking other side of bunny than reality, thats a fine example) and I am sure Samsung is doing their part as they have the literal android flagships.
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[ 3.4 ms ] story [ 266 ms ] threadhttps://en.wikipedia.org/wiki/The_Work_of_Art_in_the_Age_of_...
A digital image is a representation/abstraction/map kind of thing. And then a little bit of mental shorthand inside your head makes a connection and declares the thing and the representation-of-the-thing to be one and the same.
But it isn't the same thing. Not by a million miles.
It's a socially-accepted mindfuck is what it is.
It would be interesting to see this tried on a source image that isn't the moon. Just white with a few dark spots. Does it actually add in completely new craters, or just where there are existing smudges? Or do something like half of a moon photo and have white, does it add craters to the white side?
The OP tried to do this by changing the contrast but I failed to see any craters appearing where there wasn't already dark spots in the source photo.
It does seem strange that the OP is using an image of the moon to start and that they don't provide a still shot of the one where they modified the brightness levels to cause clipping. It doesn't really "drive the point home" as claimed.
Of course the answer to these may be that you need something moon-like enough to trigger the moon optimizations. But if that is the answer it would be interesting to see something that comes right up to the threshold where it either snaps in and out of these optimizations or two very similar images produce widely different results.
That was the point of the Gaussian blur. By blurring the source image of the moon before taking its picture, there was no information. The Gaussian blur destroys fine detail, by design.
The image enhancement applied by the Samsung phone is adding detail where there was none originally – not just detail that might be buried in the optics somewhere, but not there at all. It involves a computational model of what the target (here the moon) "should" look like and guesses at what it thinks are the blurry bits.
Sensor quality in phones goes down, AI makes up for it because good sensors are expensive, but compute time in the cloud on Samsung owned servers is cheap. You take a picture on a crappy camera, and Samsung uses AI to "fix" everything. It knows what stop signs, roadways, busses, cars, stop lights, and more should look like, and so it just uses AI to replace all the textures.
Samsung sells what's on the image to advertisers and more with the hallucinated data. People can't tell the difference and don't know. They "just want a good looking picture". People further use AI to alter images for virtual likes on Tiktok and Insta.
This faked data, submitted by users as "real pics in real places" is further used to train AI models that all seem to think objects further away have greater detail, clarity, and cleanliness than they should.
You look at a picture of a park you took, years before, and could have sworn the flowers were more pink, and not as red. You are assured, by your friend who knows it all, that people's memories are fallible; hallucinating details, colors, objects, sizes, and more. The image, your friend assures you further? "Advanced tech captured its pure form perfectly".
And thus, everyone will demand more clarity, precision, details, and color where their eyes don't remember seeing.
Another thing, pictures for proof and documentation, maybe not when they're taken but after the fact, for historical reasons, or forensics.. We can't have every picture automatically compromised as soon as it's taken. (Yes, I know that photoshop is a thing, but that's a very deliberate action, which I believe it should be)
That's me. I'm a lousy photographer, as evidenced by all of the photos I shot back when film actually recorded what you pointed it at. My photography has been vastly improved by AI. It hasn't yet reached the point of "No, you idiot, don't take a picture of that. Go left. Left! Ya know what, I'm just gonna make something up," but it should.
I imagine there will remain a use case for people who can actually compose good shots. For the remaining 99% of us, we'll use "Send the camera on vacation and stay home; it's cheaper and produces better pictures" mode.
This is fine, and I can take good shots but at the same time? I only care about this level of shot most of the time too!
But then instead of a 20MP image, which:
* takes more space, and ergo, more flash drive space
* more space to store, to backup, to send
* is made 20MP by inserting fake data
Why not have a 2MP image, which is real, and let people's end-use device "fix" it? Because all that post processing can be done when 2x or 4x the view size, too!
Because advertising.
And that's sad. We'd rather think we have a better pic, and destroy the original.
And the space thing is real. Because, that same pic gets stored in gmail with 20 people, backed up, kept in all the devices, and so on!
And the LOL of it all, is that I bet when it is uploaded to facebook... it gets downsized!
edit: in fact, my email app allows me to resize on email, so I downsize that too! Oh, those poor electrons.
There's nothing wrong with that until people start assuming they're lossless (like Huffman encoding) which they definitely are not. Unfortunately the general public doesn't understand the difference.
Yeah I've a phone with a great camera, nature shots are great, but people don't like themselves in these photos. When pressed they talk about the defects on skin and theets and eye position... Their phone beauty filters created in their mind a fake mental image of themselves and they dissociate from their real images.
It's weird. My mom brand fidelity is because Huawei specific algorithm is part of her self.
So, we’ll see what people say in a year.
[1]: https://www.wired.com/story/ai-has-a-hallucination-problem-t...
Isn't it a good thing for privacy?
Just take the story above with one more minor step: You snap a pic of the park, briefly glanced at it to make sure it wasn't blurry (which the AI would have fixed anyway) or had an ugly glare (it did, the AI fixed it) or worse a finger (the AI also fixed that).
You're satisfied the image was captured faithfully and you did a good job holding your plastic rectangle to capture unseen sights. You didn't look closely enough to notice all the faked details, because they were so good.
This fake moon super enhance? It already proves people will fall for it. I could easily see people not realizing AI turned the flowers in the picture more red, or the grass just a little too green, etc.
The story has to get stretched a lot to imagine a total dystopia, but its possible.
People like digital mirrors for all their benefits: lighter, more features, music and weather all on one "mirror". You can have voice chats, see how you might look with various makeup/styles/haircuts. This digital mirror gets so popular, and (undermining myself here a bit) high enough quality, that people want these new cool wall screens instead.
And you betcha, AI is of course going to be added. Take insta pics without needing to hold a phone, then apply filters all in one with your voice! Some people take dozens of photos, forgetting briefly how they look.
Now, people are used to every day seeing themselves in the digital mirror with minor touchups for how they would look if they used some sponsored makeup. They used to do it daily so it would match, but kept forgetting as it always showed the improved version (with a small icon saying, you are 40% matched to the predicted image!).
Some 20-something walking around in Chicago passes The Bean, and realizes they don't look quite the same as they did in their home mirror. They take out their phone to take a pic of themselves, which is of course synced to their mirror with the same "makeup enhancement suggestions", still warning them it doesn't match.
They put away their phone, confident in the knowledge The Bean is just a dirty and distorted mirror, which is why they don't look as good. The camera has always been trustworthy. Why doubt it today?
(again, fun story, I don't think this is likely. Just plausible for some people).
The contrast between what my camera raw with a stock profile puts out and what my iphone puts out is striking, and it's very clear the iphone's version of reality is optimized for Instagram and maximum color punch at the cost of looking real.
Thing is, that's what people like. So that's what we're getting.
Reality be damned.
Silicon Valley is pretty famously nice out most days. (Except for all those old strip malls in the way of the nature.)
https://www.destinationtips.com/wp-content/uploads/2018/10/P...
https://smarthistory.org/wp-content/uploads/2021/10/Giza_pyr...
https://thumbs.dreamstime.com/z/view-side-slums-egyptian-pyr...
My point is that there never has been, cameras have always let you adjust the shot - including "how red the flowers are" by changing light source, film type, shadows, which contrasting other colours are nearby, etc.
With old-school photo processing--yes, in a darkroom--you could achieve unrealistic results. But it was a choice. That's not what you got when you sent your negatives to the Costco to get printed. That's akin to the results I get when I use my camera, especially when looking at jpgs straight-out-of-the-camera.
In contrast, we get modern cellphones doing incredible processing to almost arbitrarily replace content with what some algorithm feels you'll like better, whether or not it resembles reality.
I lament that it usually resembles beginner photographer work, where they've just discovered HDR tone-mapping, local contrast, global contrast, saturation, sharpening, and smoothing filters, and promptly slam every single one of them to the stops. I did it, and now I recognize it when I see it in cellphone pics my friends send me via imessage.
Been there, done that. I recognize the stigmata of saturation slammed to the stops and excessive use of HDR and local contrast.
The default is excessive editing now, probably because it helps cover up limitations of tiny sensors, small optics, and poor exposure due to poor technique.
It does have higher than real contrast, but that's because images are 8-bit - if you don't try to fill the range, it's going to look low quality with banding artifacts.
https://9to5mac.com/2023/01/06/mkbhd-post-processing-ruining...
Larger cameras don't do that specific one as much ("dynamic range optimizer" or HDR programs do some of it), but they do care about skin in white balance and autofocus, and then photographers care when they're setting up flashes.
It's somewhat overtuned here though.
I generally just burst-mode-scan an area or scenery location and later that night, or when I add to Strava or wherever, I have an old school contact sheet (but with 60-80 images per thing) to look though. Then narrow it down to 5-10, pick the one or two I like best and discard the rest.
as AI tries to infer images of people where they aren’t really present.
The details don't quite correspond to reality; to get the framing right the AI inserted a tree branch where there wasn't one, or moved that pillar to the left to get the composition lined up. But who care? Gorgeous photo, right?
And the thing is, I don't think anyone would care. You'd get the odd weird comparison where two people take a photo of the same place and it looks different for each of them. And you'd lose the ability to use the collected photos of humanity to map the world properly.
I think it's fascinating. Reality is what we remember it to be. We can have a better reality easily ;)
It's the future. Something hit your self-driving hover car and left a small dent. To get your insurance to pay for fixing the dent you have to send them a photo.
Your camera AI sees the dent as messing up the composition and removes it.
Your insurance company is Google Insurance (it's the future...Google ran out of social media and content delivery ideas to try for a while and suddenly abandon so they had to branch out to find new areas to try and then abandon). Google's insurance AI won't approve the claim because the photo shows no damage, and it is Google so you can't reach a human to help.
Cue Paris Syndrome, because expectations will also be of a better reality. Then you go somewhere, and eat something, and experience the mess that actually exists everywhere before some AI removed it from the record.
https://en.m.wikipedia.org/wiki/Paris_syndrome
My worry about these features becoming commonplace is that if everyone just leave those features enabled, we would end up with many boring photos because they all look similar to each other. The current set of photo filters, even though they seem to be converging on particular looks, at least don't seem to invent as much detail as pasting a moon that's not there.
Edit: And the lenses on these are not your granddads computar 3-8/1.0, either. Most of the CCTV footage we see just comes from old, sometimes even analog, and lowest-bidder installations.
Edit: fixed the author's name. Cannot find the exact story though.
What actually happened was that a police officer testified that using pinch to zoom on his iPhone he saw kyle point his rifle at the first person assaulting him. Mind you we were talking about a cluster of around 5px. The state wanted to use an iPad to show the jury using pinch to zoom that same "evidence" because using proper zooming without an unknown interpolator algorithm the defense using an expert witness showed that this was not the case. No one in that courtroom understood the difference between linear and bicubic interpolation.
The defense did not understand it either so they tried to explain to the judge that the iPad Might use AI to interpolate pixels that aren't there and that the jury should only use the properly scaled video the court provided not an ad hoc pinch to zoom version in an iPad with unknown interpolation.
Thankfully the judge told the state to fuck off with their iPad but the mainstream media used the bad explanation of the defense against kyle when the reality was that the state basically tried to fake evidence live on stream using an iPad to zoom in.
BTW I'm German so I don't have a horse in the political race but I watched the Trial on live stream and saw the fake news come out while watching
This is what the defense actually said: https://www.youtube.com/watch?v=sf7xCMFBv5c
And here's the expert witness the defense brought on: https://www.youtube.com/watch?v=1GhsbizmfMs
I watched the testimony, it was accurately characterized by the person you replied to, and it was a good argument for the defense to make.
That being said, one could interpret the top comment that way.
It is all faked by AI well.....
Why do you think the stock camera apps usually get better results?
How could a phone's smaller-than-a-thumbnail lens setup ever get as much light as a proper camera's?
So a future where reality and history are subtly tweaked to the specifications of those willing to pay…
I'm working close to HW and I actively use the camera/picture and videos for future reference and debugging. It's small, fits in your pocket, and the bloody thing can record at 240fps to booth!
Until you realize there's so much post-processing done on the images, video and audio you can't really trust and can't really know if you can turn it all off. The reality is that if you could, you'd realize there's no free lunch. It's a small sensor, and while we had huge improvements in sensor and small lenses, it's still a small sensor.
Did the smoothing/compression remove details? Did the multi-shot remove or add motion artifacts you wanted to see? Has noise-cancelling removed or altered frequencies? Is the high-frame rate real, interpolated, or anything inbetween depending on light just to make it look nice?
In the end, they're consumer devices. "Does it look good -> yes" is what thrums everything in this market. Expect the worst.
This has been true of consumer digital cameras for 25 years. It's not new to or exclusive to smartphone cameras. It's not even exclusive to consumer cameras as professional ones costing many times more also do a bunch of image processing before anything is committed to disk.
And they actually look good!
No phone can deliver that, even today.
Sony is probably the worst offender here actually. The a6x00 series applies lossy (!) compression to every RAW file without any option to disable, and there's an additional noise filter on long exposures that wreaks havoc on astrophotography:
https://stephenbayphotography.com/blog/sony-raw-compression-...
http://www.markshelley.co.uk/Astronomy/SonyA7S/sonystareater...
"You just took a picture of the Eiffel Tower. We searched our database and found 2.4 million public pictures taken from the same location and time of day. Here are 30,000 photos that are identical to yours, except better. Would you like to delete yours and use one of them instead?"
edit: for a while anyway
https://en.wikipedia.org/wiki/Ron_Popeil
The current narrative in financial places seems to be 2023 is gone but 2024 comes with a vengeance, feel safe investing, your real estate will grow in value! We're making people go back to the office so that it does! More lemmings! Less quality! More production!
Yes. I'm peak cynical after 3 years of self made economic destruction only to tell those who didn't cause it that "of course they must pay for the broken plates".
Also, everytime someone invests a lot in explaining themselves in HN, if it goes against the current thread narrative, it'll still be ignored so it's not like it's particularly worth it. HN feels more Reddit and less Slashdot of old.
Still a nice aggregator to find stuff that might peak interest.
You got a friend, spouse or someone close that has hundreds of pictures of you on their phone. Their phone has a "AI chip" that is used to finetune the recognition models and photo models with your AI library. Like Google Photos tags images of people you know, so does the model. It also helps sharpen images - you moved your head in an image and it was a bit blurry, but the model just fixed it, because like the original model had for the moon, it has hundreds of pictures of you to compensate.
One day, that person witnesses a robbery. They try and take a photo of the robber, but the algorithm determines it was you on the photo and fixes it up to apply your face. Congratulations, you are now a robber.
Once generative AI really takes off we will need some system for unambiguously proving where an image/video came from; the solution is quite obvious in this case and many have sketched it already.
In a world where any image or video can be generated, chain of custody to a real-world ground-truth will be vitally important.
This will become indispensable sooner than anyone imagines or wants it to.
Absent a chain of custody (perhaps including GPS baked into the signed image data blob), I think analog artifacts will become untrustable. Unless you can physically date them to pre-generative era!
Bonus feature: new display devices get better trained / new AI features for free
E.g. from the raw data we know the sensor, we can train a specific AI to enhance the image from the known physical properties of the CCD
Sometimes there are exploits which extract these keys from the cameras themselves, but I don't hear them nowadays.
One of the older products: https://imaging.nikon.com/lineup/software/img_auth/index.htm
Likewise depth fields and other potential forms of sensor augmentation.
For the long time digital cameras embedded in EXIF metadata about conditions on which the photo was made. Like camera model, focal length, exposure time etc
Nowadays this metadata should be extended with description of AI postprocessing operations.
Of course. But to ensure that's valid for multiple purposes we need a secure boot chain, and the infrastructure for it.
To get there we need an AI arms race. People trying to detect AI art with machine learning vs. increasing AI sophistication. Companies trying to discourage AI leaks of company secrets and reduce liability (and reduce the tragic cost of mistakes of course) vs. employees being human.
Or we could have built a responsible and reasonable government that can debate and implement that.
Maybe I'm naive. I'll take responsibility for that.
In the meantime, it's playtime for the AIs. Bring your fucking poo bags, theyre shitting everywhere (1), pack it in, pack it out.
(1) what the world didnt know, was that this was beautiful too.
Or we can just recognize the lunacy of it and opt out of caring. You can't stop the flood, so you just learn to live with it. With the right view, the flood becomes unimportant.
What measures can the government implement to combat this? AI image modification is realistically possible even on consumer hardware running locally. There is no going back.
Some image formats (e.g. HEIF) already allow to store multiple images in the same file.
https://news.ycombinator.com/item?id=29223815
This "future" is present in current Pixel lineup btw. Photos are tagged as unblured, so for now you can still safely take a selfie with your friends.
Photos taken by cell phone cameras increasingly can't be trusted as evidence of the state of something. Let's say you take a picture of a car that just hit a pedestrian and is driving away.
Pre-AI, your picture might be a bit blurry, but say, it's discernible that one of the headlights had a chunk taken out of it; it's only a few pixels, but there's obviously some damage, like a hole from a rock or a pellet gun. Police find a suspect, see the car, note damage to the headlight that looks very close, get a warrant for records from the suspect, find incriminating texts or whatnot, and boom, person goes to jail for killing someone (assuming this isn't the US, where people almost never go to jail for assault, manslaughter, or homicide with a car) because the judge or jury are shown photos from the scene, taken by detectives in the street of the person's driveway, and then from evidence techs nice and close-up.
Post-"AI" bullshit, the AI sees what looks like a car headlight, assumes the few-pixels damage is dust on the sensor/lens or noise, and "fixes" the image, removing it and turning it into a perfect-looking headlight.
Or, how about the inverse? A defense attorney can now argue that a cell phone camera photo can't be relied upon as evidence because of all the manipulation that goes on. That backpack in a photo someone takes as a mugger runs away? Maybe the phone's algorithm thought a glint of light was a logo and extrapolated it into the shape of a popular athletic brand's logo.
Yeah, but in the future the government will know your precise location, all day, every day, so at least you'll have an alibi.
AI isn't the thing to be worried about. People with power abusing AI is the thing to be worried about.
Sounds like pretty standard forensic science, like bite marks and fingerprints.
If any of you young folks haven't watched Ghost in the Shell, just close this tab and do that.
[1] https://ghostintheshell.fandom.com/wiki/Laughing_Man
http://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_...
https://news.ycombinator.com/item?id=29223815
https://news.ycombinator.com/item?id=6156238
So far as law and justice goes it is the other way around too. If it is known to be possible that cameras can hallucinate your identity, it won't be possible to use photographic proof to hold people to account.
This of course isn't always the case. When something is really important or significant people sometimes do want to know the truth as best they can. I want to know the car I'm purchasing isn't a lemon, I want to know the home I'm buying isn't a money pit, I want the doctor to to tell me if my health is good or bad (for some, under the condition the information is actionable), and so on.
When it comes to more frivolous things, for many, build the fantasy, sell them that farm to table meal you harvested from the dew drops this morning and hand cooked with the story of your suffering to Michelin star chef and how you're saving my local community by homing puppies from the local animal shelter with profits... even if you took something frozen, slapped it in the microwave and plated it and just donate $10 a month to your local animal shelter where you visited twice to create a pool of photos to market. For many, they want and crave the fantasy.
Progress made by science and tech has, for a brief fragment of history, established techniques and made practical, in some cases, to peel away all or at least some layers of fantasy away to reality. We started to pierce into cold hard reality and separate the signal of truth, as we can best understand it, from all the noise of ignorance and fantasy.
For many fantasy lovers, snakeoil salesmen, and con men, pulling away the veil of fantasy and noise has been a threat and there's been a consistent battle to undermine those efforts. The whole emergence and perpetuation of misinformation and recent "fake news" trends are just some of the latest popular approaches. We've been seeding our knowledge and information more recently with increasing degrees of falsehoods and pure fabrications.
Now, enter "AI," especially generative flavors. The same people who wanted to undermine truth are foaming at the mouth at the current ability to produce vast amounts of noise that in some cases are almost indistinguishable from reality from current techniques we have. Not only that, fantasy lovers en masse are excited at the new level of fantasy they can be sold. They really really don't care or want the truth. They really do just want "a good looking picture", "to make the summary interesting", or just see some neat picture. They don't care how accurate it is. Now people interested in the truth are facing a deluge of technologically enabled difficult to seperate noise production.
Is what I'm looking at close to reality? How many layers of noise are there I should consider when interpreting this piece of information? In the past, the layers used to be pretty managable, they were largely physical limitations or resource limitations to falsify the data to a point that couldn't be easily discerned. These days... it's becoming increasingly difficult to determine this and more and more information in various forms are leveraging more sophisticated and believable noise production. Technology has made this affordable to the masses and there are many parties with interest in setting the clock back to a world where the best story tellers are looked at as the oracles of modern time.
People often scoff at ChatGPT that it seeds or "hallucinates" to interpolate and extrapolate gaps of knowledge and make connections but it does so in a way that people like. It projects confidence, certainty, and in many cases it gives exactly what people want. To me, it's scary because it's providing a ...
The future is BigCorp's AI will censure your photo when you try to take picture like on tiananmen square massacre.
I want a photo, not even sharpend.
Eventually people won’t care much for clarity and precision, that’s boring. The real problem is that everything that can be photographed will eventually have been photographed in all kinds of ways. What people really want is just pictures that look more awesome, in ways other people haven’t seen before.
So instead, raw photos will be little more than prompts that get fed to an AI that “reimagines” the image to be wildly different, using impossible or impractical perspectives, lighting, deleted crowds, anything you can imagine, even fantasy elements like massive planets in the sky or strange critters scurrying about.
And thus, cameras will be more like having your own personal painter in your pocket, painting impressions of places you visit, making them far more interesting than you remember and delighting your followers with unique content. Worlds of pure imagination.
There's an arms race between people adding nonexistent details to old films and people manufacturing televisions with dense enough pixels to render those microscopic fictions. Then they lay a filter over it all and everything becomes smooth gradients with perfectly sharp edges.
In the other hand, there will likely be a market for makeup mirrors that emphasize your flaws (both to help the user and to sell more product)
AI will accelerate this process exponentially and just like in The Matrix, most people will eventually prefer the simulation to reality itself.
<soapbox>confabulated</soapbox>
Of course they would have perfect skin and expertly applied eye-liner and lipstick as well.
A "smart" scanner tries to compress images.
Not sure I like that future
A combination of ai models trained on high resolution textures and objects, models of the actors, and training from every frame of the movie that cal use the textures and geometry from multiple angles and cameras to “reconstruct” lost detail.
I am begging people to find a new gotcha for AI other than "training it on data created by itself or other AI."
It's an obvious issue with obvious solutions. If it happens, it will be due to ignorance. It is not inevitable, and it shouldn't even be likely.
So in the future, there are either cameras that can see what others have seen before, and those that can truly capture new, true, detail (true as in, without filling it with estimations)
The Gaussian blur they applied is theoretically reversible in a continuous function and infinite precision. In a discrete function (like a image in the computer) and only a few dozens of bits it's not 100% reversible, but it can be partially undone and get a sharper image (that is not as sharp as the initial image).
But there still might be advanced machine learning models, rather than simple filters.
If they applied a perfect digital gaussian-blur, then that is reversible (except at the edges of the image, which are black in this case anyway). You still lose some detail due to rounding errors, but not nearly as much as you might expect.
A gaussian blur (and several other kinds of blur) are a convolution of the image with a specific blur function. A convolution is equivalent to simply multiplying pointwise the two functions in frequency space. As long as you know the blur function exactly, you can divide the final image by the gaussian function in frequency space and get the original image back (modulo rounding errors).
It is not totally inconceivable that the AI model could have learned to do this deconvolution with the Gaussian blur function, in order to recover more detail from the image.
(Just in case, original image https://imgur.com/PIAjVKp )
Those rounding errors are very important though. The Gaussian function goes to zero very quickly and dividing by small numbers is not a good idea.
If your deconvolving a noise free version of the original that also doesn't have any saturated pixels (in the black or white direction) then you can get the pretty close to the original back. I don't think this applies here because the OP is taking a picture of a screen that shows the blurred version, so we've got all kind of error sources. I think the OP is right: the camera is subbing in a known picture of the moon.
It would be interesting to see what happens with anisotropic blur for example, or with a picture of the moon with added fake details (words maybe?) and then blurred.
> To further drive home my point, I blurred the moon even further and clipped the highlights, which means the area which is above 216 in brightness gets clipped to pure white - there's no detail there, just a white blob - https://imgur.com/9XMgt06
> I zoomed in on the monitor showing that image and, guess what, again you see slapped on detail, even in the parts I explicitly clipped (made completely 100% white): https://imgur.com/9kichAp
Given how small the pure-white areas are, tbh I'm not sure I'd consider that as having "added detail". It has texture that matches the rest of the moon, but that's about as far as I'd be comfortable claiming... and that seems fine, basically an un-blurring artifact like you see in tons of sharpening algorithms.
I do think this "clip the data, look for impossible details" is a very good experiment and one that seems likely to bear fruit, since it's something cameras "expect" to encounter. I just don't think this instance is all that convincing.
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And to be clear, I absolutely believe Samsung is faking it, and hiding behind marketing jargon. The outputs are pretty ridiculous. They may not be "photoshopping on a texture", but training an AI on moon pictures and asking it to add those details to images is... well, the same thing Photoshop has features for. It makes no difference - it's not maximizing the data available, it's injecting external data, and they deserve to be slammed for that.
Well yes, but he also downsampled the image to 170x170. As far as I know, downsampled information is strictly lost, and unrecoverable without an external information source (like an AI model trained with pictures of moon).
[1] https://upload.wikimedia.org/wikipedia/commons/thumb/2/2b/Lu...
[2] https://imgur.com/bXJOZgI
Rather, reversing blur of any type is limited (a) by spatial decimation (a.k.a. down sampling, which is performed in the article), and (b) by noise/quantization floor, below which high frequency content has been pushed.
Another point of view is that there are infinitely many images that will produce the same result after blurring. Obviously, this makes the operation irreversible.
No, it's emphatically not. Perhaps you are thinking of displaying a spectrogram.
To produce an image from frequency-domain data, inverse DFT must be applied. Since (as @nyanpasu64 points out), the DFT of a real-valued image or kernel is conjugate-symmetric (and vice-versa), the result is again real-valued without loss of information. The phase information is not lost. If it were, the image would be a jumbled mess.
(Not that DFT+inverse DFT is necessary for Gaussian blur anyway -- you simply convolve with a truncated Gaussian kernel.)
> Another point of view is that there are infinitely many images that will produce the same result after blurring.
No, this is not true. I don't know why you think it is. This is only true of a brick wall filter, which Gaussian filter is not [1].
The SNR of high-spatial-frequency components is reduced for sure, which can lead to irrevocable information loss. But this is nothing to do with phase.
[1] https://en.wikipedia.org/wiki/Window_function#Gaussian_windo...
[1] https://en.wikipedia.org/wiki/Window_function#Gaussian_windo...
[2] https://en.wikipedia.org/wiki/Wiener_deconvolution
https://news.ycombinator.com/item?id=35111998
Actually any noise distribution is frequently reversible if you know the parameters and number of steps. This is in fact how diffusion models work (there's even work of Normalizing Flows removing realistic camera noise). It is just almost impossible to figure this out since there are many equivalent looking ways. But we need to be clear that there is a difference between reversibility and invertibility. A invertible process is bijective, or perfectly recreates the original setting. A reversible process can just work in both directions and isn't guaranteed to be invertible. (Invertible means reversible but reversible doesn't mean invertible)[0]
I bring this up because even more complicated versions of bluring could be argued as not "faked" but rather "enhanced." A better way to test Samsung faking the data is to mask out regions. If the phone fills in the gaps then it is definitely generating new data. This can still be fine if the regions are small, unless we also want to call bilinear interpolation "faked" but I don't think most people would. This is why it gets quite difficult to actually prove Samsung is faking the image. I don't have a Samsung phone to test this though.
So basically I'm with you, and even a slightly stronger version of this
> It is not totally inconceivable that the AI model could have learned to do this deconvolution with the Gaussian blur function, in order to recover more detail from the image.
Edit: After reading other comments I wanted to bring some things up.
- The down scaling is reversible, but not invertible. We can upscale, reversing the process. But yes, there is information lost. But some data can still be approximated and/or inferred.
- The clipping experiment isn't that good. Honestly, looking at the two my brain fills in the pieces and they look reasonable to me too. Clipping the brightness isn't enough, especially since it is a small portion of the actual distribution. I did this on both the full image and small image and both are difficult to distinguish by eye from the non-clipped. Clipping below 200 seems to better wash out the bottom of the moon and remove that detail. 180 seems better though tbh.
> But yes, there is information lost. But some data can still be approximated and/or inferred.
The perfect summary.
A smartphone sensor pixel has space for some low 4 digits number of electrons (created with some probability from photons, but that stochastic effect doesn't matter for anything a normal user would photograph) and typically should have a fixed 2~10 electron standard deviation from the analog-to-digital-converter (well, mostly the amplifiers involved in that process).
So if your pixel is fully exposed at a high 10000 electrons, and you √ that, you have 100 electrons stddev from shot noise plus worst case 10 electrons stddev from the readout amplifier/ADC. If you have a dark pixel that only got 100x less light to only have accumulated 100 electrons, √ of that gives 10 electrons stddev of shot noise plus the same 10 electrons stddev readout amplifier/ADC.
The problem is that while you have an SNR of 5 with the dark pixel, when trying to deconvolve it out of a nearby bright pixel, even perfectly with no rounding errors (1 electron = 1 ulp/lsb in a linear raw format), you now have 100/110 = 10/11 ≈ 0.91. That's far worse than the 5 from before. This gets worse if your ADC has only the 2 electrons stddev instead of the 10 (about 2x worse here).
That's the reason why deconvolution after the photon detector is a band aid that you only begrudgingly tend to accept.
The trade-off just requires massively increased aperture/light gathering, likely negating your savings on optics.
Not true. Deconvolution is a statistical estimate. Think about it. When you blur, colors get combined with their neighbors. Statistically this moves toward a middle grey. You're compressing the gamut of colors towards the middle, and thus losing information. Look at an extreme case - 2 pixels of mid-grey. It can be deconvoluted to itself, to a light and dark grey, or to one black and one white. All those deconvolutions are equally valid. There's no 1-to-1 inverse to a convolution. If you do a gaussian blur on a real photo and then a deconvolution algorithm you'll get a different image, with an arbitrary tuning, but probably biased towards max contrast in details and light noise, since that what people expect from such tools and what most real photos have. But, just like A.I. enhanced images, it's using statistics when filling in the missing data.
I wonder if there is some algorithmic way to find the key and tell if it's correct - some dictionary attack, or some loss function that knows if it's close. Perhaps such a thing only works on images that are similar to a training set. It wouldn't work on black and white random dots, since there'd be no way for a computer to grade or know statistics for which deconvolution looks right.
Given a perfect blurred image, reconstruction is possible - however due to the attenuation, these high frequency components are ~sensitive~.
Apart from quantisation effects [you mentioned which limits perfect de-convolution], adding a little AW Gaussian noise(such as taking a photo of the image from across the room) after the kernel is applied obliterates high frequency features.
Recovery when noise is low (plus known glyphs) is why you should not use Gaussian blur followed by print screen to redact documents. Inability to recover when there are artifacts and noise is [part of] why cameras cannot just set a fixed focus [at whatever distance] and deconvolve with the aperture [estimated width at each pixel] to deblur everything that was out of focus.
TLDR for readers, It is unlikely to recover sufficient detail via de-convolution here.
You can get sued if you publish someones real pictures without makeup and photo-shopping. AI beatified pictures have the same criteria.
That sounds really weird. Any further info?
I’ve taken pictures of the moon at 100x optical zoom, and if Samsung is really faking this they’re doing a truly awful job of it.
AI has been used in "cell phone photography" for a few years, at lease since Pixel 2 where a mediocre sensor produced much better pictures than what people expect (maybe there are other players who did this even earlier). And every manufacturer started doing it, including Apple. Otherwise, do you think "night mode" is just pure magic? Of course not, algorithms are used everywhere.
How do you define "fake"? In podcasts, Verge editor Nilay Patel has asked various people "what is a photo", because the concept of a "photo" has become increasingly blurry. That is the question the author is asking, and people may have different answers from the author's.
Curiously and revealingly, the political word photo-op stands alone in this photo- parade of words in the age of photo-imaginings. The universe does indeed have a sense of humor.
Night mode definitely uses some AI but most of the result is from stacking frames. Samsung here did not label it as a "scene optimizer". Their marketing just calls it Space Zoom. The only disclaimer they provide is "Space Zoom includes digital zoom, which may cause some image deterioration."
The problem is that AI isn't just interpolating data. It is wholesale adding extra data that simply doesn't exist. The person in the background is facing left, but the sensor couldn't possibly have captured that detail even after multiple images--it was a coin flip that the AI made.
The issue is that, like privacy, most people won't care ... until they do. By that time, it will be too late.
Someone here included an example where it does do something like this: https://news.ycombinator.com/item?id=35109568
Is is ridiculous that OP consider this "cheating". Most people just want a nice picture and don't give a damn about AI.
nice.
I'm sure that they have started using AI to fill in details more recently, but this is just to point out clever use of multiple exposures and AI can help without faking detail.
Strictly speaking, applying a Gaussian blur does not destroy the information. You can undo a Gaussian blur with a simple deconvolution, which is something I would expect even a non-AI image enhancement algorithm to do (given that, you know, lenses are involved here).
I'd like to see what detail can be "recovered" with just the downsizing, which DOES destroy information.
Binning the image, or cropping it in Fourier space, would be a better test.
All the subtle trickery manipulation that the smart phone's doing to reality is concerning. Smoothing people's faces, making their eyes pop, enhancing the shit out of the colours, and now plopping fake objects overtop of the real ones.
Future concerns of this technology should range from a low-key disconnect from reality, to the complete inability to photograph certain objects or locations.
Imagine dusting off a 30 year old digital camera, finding some AA batteries to put in it, snap a selfie and then realizing just how ugly we all are and how washed out the polluted world actually looks without a bunch of narcissism-pandering enhancements.
This phrase could cast a broadening net with each year’s new tech.
Our brains do far worse stuff with our memories. Not sure how relevant 'high fidelity' is to people who mostly use phones for memories
Compare this to AI/ML-manipulated photos that aren't labelled as such. In this case we no longer know if it's our brain fudging a picture or our camera, so we are starting to lose an arbiter of truth.
Side-rant but it'll be weird when this makes it into high-end cameras that save RAW images. Will RAW still be "raw?" I don't know enough to say.
"The Hottest Gen Z Gadget Is a 20-Year-Old Digital Camera
Young people are opting for point-and-shoots and blurry photos."
https://www.nytimes.com/2023/01/07/technology/digital-camera...
I think it's great. It's the exact opposite of the person who spends all their time gear shopping and never using the gear.
The remaining budget can go into going to nice places to shoot nice photos, and to print them nicely and large enough to put on your wall choice.
I picked Nikon, but I assume Canon works as well.
The G9x Mark II, Sony RX100, Panasonic LUMIX and similar 1" sensor cameras are awesome though and I don't think they've gotten too crazy with computational photography. I imagine some color processing modes might be doing a bit of work though.
Sensor: 23.5 x 15.6mm 24MP
S21 Ultra: https://cdn.discordapp.com/attachments/1010562706237038633/1...
Telephoto sensor: 3.3 x 4.3mm 10MP (240mm equivalent)
Compare side by side: https://imgur.com/a/QwnV99D
This is to get an idea of quality, with the APS-C camera having a much larger (26x area) sensor and better optics to work with.
My impression is... S21 Ultra "space zoom" is, at best, a good party trick. But if you zoom in, the quality is still nearly garbage. Not an objectively "great photo of the moon."
It still shows you much more details than visible via eyes, so yeah its a party trick (what else would moon shot on phone be), but pretty darn great at that (I haven't seen so many people with :-O since iphone 1 release when showing this... then they quickly try their top iphones and xiaomis and end up consistently with a small white blob).
There is one aspect that this phone wins at easily - it can take that moonshot (TBH it can be a bit sharper than yours) while handheld, pretty consistently. Good luck trying that with your apsc with such a long shutter, it will consistently end up in just a blur. Software often beats raw hardware even these days.
https://cdn.discordapp.com/attachments/1010562706237038633/1...
I can clearly see that most folks here don't actually own discussed devices (which is fine, its US-based HN, a bastion of iphone and many Apple employees dwell here and uncritical appreciation of Apple is very evident in every single related thread). I've used its 10x zoom extensively over more than a year, it simply blows all other phones away easily for that kind of situation (more than those rather weak 3x zooms available everywhere). Family photos, wild animals, nature, anything you want to come closer, otherwise the scene is tiny dots in the center like on other phones. It works really well for what it is, with obvious unavoidable physical limits.
Overall this phone made me put my fullframe Nikon D750 away on a day I bought it. I took it 'just to be sure' on vacation to Egypt last year, didn't touch it a single time. Most often it doesn't produce strictly as good images but a) they are good enough to be viewed on phones side by side easily, basically as good as fullframe there and sometimes even much better, ie handheld photos in the night of dark scenes, fullframe is utterly lost without tripod, and b) it weights 0 and takes 0 extra space (and cost 0 instead of many thousands for modern camera with big sensor), since I have phone with me always anyway.
Tried exactly the steps as author of article, couldn't reproduce it a bit, tried various mega zooms, his various original photos, dark room etc. Blur remained blur, nothing added. I mean at this point everybody acknowledges any decent phone is painting quite a bit (ie iphone taking other side of bunny than reality, thats a fine example) and I am sure Samsung is doing their part as they have the literal android flagships.