The point is worth belaboring because people have a tendency to take the output from these systems as Truth, and while they can be interesting and useful, they should not be used for things for which the truth has consequences without understanding their limitations.
You're right to compare this to how our brains reconstruct our own memories, and the implications that has for eyewitness testimony should inform how we consider the outputs from these systems.
This might seem like a quibble, but once you dive a little deeper into it, you realise that there's enormous latitude and subjectivity in the way you do that interpretation.
What's even crazier is that this didn't come with digital photography. Analogue film photography has the same problem. The silver on the film doesn't become an image until it's interpreted by someone in the darkroom.
There is no such thing as an objective photograph. It's always a subjective interpretation of an ambiguous record.
With ML-enhanced photos, you might have a distanced face that is "enhanced" by the model, to become a face that wasn't there. Or a fingerprint, a birthmark, a mole, etc.
The nice thing about this was that you could hand the E-6 off to a magazine and end up with a photograph printed in the magazine that was very close to the original film. Any color shifts or changes in contrast you could see just with your eyes. You could drop the film in a scanner and visually confirm that the scan looks identical to the original. (You cannot do this with C-41.)
This was not used for forensic photography, though. The point of using E-6 was for the photographer to make artistic decisions and capture them on film, so they can get back to taking photos. My understanding is that crime scene photography was largely C-41, once it was relatively cheap.
1. However good the guess is, it's still just that: a guess. Taking the standard of "evidence in a murder case", the OCR can and probably should be used to point investigators in the right direction so they can go and collect more data, but it should not be considered sufficient as evidence itself.
2. OCR is a relatively constrained solution space - success in those conditions doesn't mean the same level of accuracy can or will be reached outside of that constrained space.
To be clear, though - I'm making a primarily epistemic argument, not one based on utility. There are a lot of areas for which these kind of machine guessing systems are of enormous utility, we just shouldn't confuse what they're doing with actual data collection.
But the issue manifests as characters being incorrectly identified because of an algo t
Edit - re OCR do you mean e.g. from a picture of a blurred license plate we could rule in or out a subset of possible numbers, depending on how blurred, like a B could be a 8 but not a L? (And sorry if your example is unrelated). This is valid, and unrelated to super resolution, you can do this analysis with Nyquist and point spread functions.
I don't. Everyone knows this already and it seems like a lot of people are just saying it over and over to look clever.
It's nice for a casual Instagrammer, but then a lot of science and engineering also gets done using COTS equipment. I worry that at some point, a lot of money will be burned, a lot of time wasted, or even lives lost, because someone didn't notice they've based the conclusions of their scientific experiment/engineering analysis on such "computer best guesses". As a researcher, you'll see a weird pattern on some of the photos and will be left wondering, is that a real phenomenon, or is it just one of the black box, trade secret neural networks in the camera choking on input data it wasn't trained for?
https://www.theregister.com/2013/08/06/xerox_copier_flaw_mea...
It's not too dissimilar, I agree, but there are differences.
> a small usually collapsible bed often of fabric stretched on a frame
But in this case, COTS is apparently...
> commercial, off-the-shelf
In other words "photo equipment" or "consumer/retail photo equipment."
Upon further reading[0] it seems odd to use the term here, but maybe I'm misunderstanding something. It's often used for software and has a key phrase...
> packaged solutions which are then adapted to satisfy the needs of the purchasing organization
But it's possible the term has been co-opted to mean something else now.
"Commercial off-the-shelf or commercially available off-the-shelf[1] (COTS) products are packaged solutions[buzzword] which are then adapted to satisfy the needs of the purchasing organization, rather than the commissioning of custom-made, or bespoke, solutions."
So for example, a research team may decide to not spend money on expensive scientific cameras for monitoring experiment, and instead opt to buy an expensive - but still much cheaper - DSLR sold to photographers, or strap a couple of iPhones 15 they found in the drawer (it's the future, they're all using iPhones 17, which is two generations behind the newest one). That's using COTS equipment. COTS is typically sold to less sophisticated users, but is often useful for less sophisticated needs of more sophisticated users too. But if COTS cameras start to accrue built-in algorithms that literally fake data, it may be a while before such researchers realize they're looking at photos where most of the pixels don't correspond to observable reality, in a complicated way they didn't expect.
In the novel, quantum computers (rather than ML per-se) are tasked with interpolating more and more detailed data from astronomical observations, to the point that tracking individual members of an alien species on a distant world, underground, is possible. Eventually it is noticed that cutting off the astronomical data entirely doesn't interrupt the interpolated data. Then things get weird.
I won't go into further plot details, as that would be spoilery, but it is a pretty good book, reminiscent to me of Greg Egan's oeuvre (the novel is actually by Robert Charles Wilson).
As an aside, the term of art is "make-or-buy" if you want to be able to Google it.
The discussion we are having is interesting because COTS are notorious for their hidden costs and how difficult they are to properly budget. Having to find a way to disable or reverse advance post-processing in a camera would be a fairly typical example of that. In this specific case it might mean having to commission a custom firmware from the camera manufacturer - something which is very much doable but might end up costing you as much as buying bespoke equipments for inferior results in the end.
Make-or-buy seems more a term for manufacturing industry/SCM. TIL
Most research papers are crap anyway, in a much more fundamental way and for much worse reasons/bad incentives with far more impact than "computational imaging".
This is probably the last thing I'd worry about when thinking about "millions/time/wasted" for some research.
I personally take a lot of high resolution art photos. One that is deeply in my memory is a picture I took of the Manhattan bridge from the Brooklyn side with a 4x5 camera. I can get out the negative and view it under magnification and read the street signs across the river. (I would link you, but Google downrez'd all my photos, so the negatives are all I have.) ML upscaling probably won't let you do that, but on the other hand, it's probably pointless. It's not something that has a commercial use, it's just neat. If you want to know what the street signs on the FDR say, you can just look at Google Street View.
(OK, maybe it does have some value. I used to work in an office that had pictures blown up to room-size used as wallpaper in conference rooms. It looked great, and satisfied my desire to get close and see every detail. But, you know you're taking that kind of picture in advance, and you use the right tools. You can rent a digital medium format camera. You can use film and get it drum scanned. But, for people that just need a picture for an article, fake upscaling is probably good enough. The picture isn't an art exhibit, or an attempt to collect visual data. It's just something to draw you into the article in the 3 milliseconds before you see a wall of text and bounce.)
Wow, Google ate your one digital copy? That's tragic.
What's the approximate resolution you could get out of a scan from these labs?
I was interested in getting into film cameras at one point, and I was disappointed with how low the scanning resolution is from most labs. For example mpix only advertises 18MB, which they say is good enough for a 12in by 18in print. North Coast Photo (what Ken Rockwell recommends) is even worse! What if you want something to put on the wall?
Granted, if the original film you shoot is perfect you can have a print done from the negatives, but that kind of defeats the point of having a high quality scan as a backup.
With my home setup, I can easily do 50-80 megapixels on a 4x5 negative. I use a flatbed photo scanner (the Epson V800) and wet-mount the film. It is Quite The Process involving a lot of parts (liquids, optical film to place on top of the mount, careful calibration of the focus point, etc.) but the results are excellent and relatively repeatable. But, all in, I'd estimate that it's probably a half hour of labor per photo, so you can see why labs charge so much. (Dry mounting doesn't save that much time, because of the amount of time you spend avoiding dust and optical artifacts intrinsic in using two extra sheets of glass.)
The real professionals use drum scanners. They are quite expensive, but offer incredibly high resolution and decent throughput for the operator. Looking around at prices, for $100 you can get a 320MP scan of a 4x5 negative yielding a 1.7GB file. http://www.drumscanning.com/rates.html For fine grained black and white films, you can certainly extract information that actually exists. As someone who mostly uses T-Max 400, though, that would be overkill. I can't imagine getting much more information out of my photos than I get with a flatbed.
In summary, you can see why even pixel peepers are content with their Sony A7R. Press button, get 50 megapixels. And no toxic chemicals being absorbed through your skin.
Looks like you can get those scanners used for pretty reasonable prices. Maybe if I've got a house one day and I think the odds of having to move within a few years are low I'll get into it and try setting up a lab.
> In summary, you can see why even pixel peepers are content with their Sony A7R. Press button, get 50 megapixels. And no toxic chemicals being absorbed through your skin.
Yep, and on top of that we're not limited by the sRGB gamut or bit depth issues of early digital cameras. Recent ones produce raw files that are extremely easy to develop and manipulate into something very nice looking.
The thing is, even on top of the enjoyment some people get out of working with film, if you're after a particular film-like look you might be able to save yourself a significant amount of post-processing time by just going with film. I've seen no one-click filter that can approximate it.
Never mind memories; there are parts of our eyes that aren’t responsive to light at all. We’re always hallucinating.
Are you referring to the blind spot, or something else?
Interestingly, the blind spot turns out not to be a design requirement, it is a contingent feature that cephalopods like octopuses (whose eyes evolved independently from vertebrates') don't have.
It’s much easier for the model to blow up uninteresting pieces of the photograph than interesting pieces.
Technically, they were correct -- it wasn't her. It was an algorithm's best-guess reconstruction based on training data of other people's faces. Unfortunately, neither the original poster or anyone else in the thread seemed to grasp this concept.
When playing around funny things happen too: recursive upscale/sharpen and analogue artifacts begin resembling topography, molten metal etc.
Now imagine this being fitted into military drones, which it almost certainly is.
Thoughts?
1x1 0-0-0 (original)
1x2 0-0-1 (made up)
1x3 0-0-2 (original)
These methods are already in extensive use (most smartphone images use extensive noise-reduction techniques), but we must be ever-cognizant that image-processing techniques can add yet another layer of nuance and uncertainty when we try to understand an image.