It would be good to get a comparison of a brightened version of the sample image compared with the CNN version. Right now the sample image just looks black, but if you scale up the brightness you get an image that looks more like the higher ISO image. That would be a better comparison since it shows what improvements the CNN gives over naive techniques like just bumping up the pixel values.
Learning to See in the Dark (2018)
111–120 of 179 posts
Re: Learning to See in the Dark (2018)
#112I wonder, how much can it improve over this: https://youtu.be/c_0s06ORTkY
X27 is also using some kind of neural algorithm to denoise and get maximum out of the CIS
Re: Learning to See in the Dark (2018)
#113The problem with techniques like this is that they fundamentally amount to ‘making a plausible guess as to what the image would look like’, since essentially they can’t extract information that is simply not there. There is a Shannon entropy limit here. Machine learning is really machine-enhanced educated-guesswork, which has its place but also has its limits.
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Re: Learning to See in the Dark (2018)
#114As a photographer, the comparison to "raw" results without color balance or noise removal seems somewhat deceptive. The effects visible in the video seem easy to quickly replicate with existing techniques, such as the "surface blur" filter that averages out pixel values in areas with similar color. This happens at the expense of detail in low-contrast areas, producing a plastic-like appearance of human skin and hair,…
>As a photographer, the comparison to "raw" results without color balance or noise removal seems somewhat deceptive.
Huh? At 1:40 in the video that's exactly what they do.
Re: Learning to See in the Dark (2018)
#115Discussed at the time: https://news.ycombinator.com/item?id=17064079
Re: Learning to See in the Dark (2018)
#116Interestingly, this is effectively an extreme version of solving the colorization of black & white photos problem. I wonder what the results would be if you just threw some black and white photos into the model.
Re: Learning to See in the Dark (2018)
#117Impressive of the American news channel, CNN, to convert images in minus one second.
Please don't do this. What would be a fun crack in person becomes a plastic garbage patch here.
Re: Learning to See in the Dark (2018)
#118A "Two Minute Papers" on this project from 2018: https://www.youtube.com/watch?v=bcZFQ3f26pA
Re: Learning to See in the Dark (2018)
#119I always wondered if you can "trust" an image that has been basically recreated. Could that kind of image be used as an evidence in court?
If you could show a basic level of consistency. Take the correctly rendered title on the book vs the incorrect colors; the odds that it got lucky with the text instead of a different title or a book with random letters on the cover are negligible. But if your evidence revolved around the color of the book the villain stole from the library, not so much.
So if you're planning to do crime, make choices where the evidence relies on spectra rather than geometry. Steal Rothkos rather than Mondrians; baggy coveralls are in, form-fitting ninja wear is out.
Re: Learning to See in the Dark (2018)
#120The problem with techniques like this is that they fundamentally amount to ‘making a plausible guess as to what the image would look like’, since essentially they can’t extract information that is simply not there. There is a Shannon entropy limit here. Machine learning is really machine-enhanced educated-guesswork, which has its place but also has its limits.
It's more than 'good enough' for most purposes. Matching other shots for Hollywood quality, probably not. For surveillance or the like it's fine. The things it's guessing poorly about are textures or colors. Being able to read the title on the books in the example photo is great; you could rely on the title for evidentiary purposes, the smaller text probably not so much. So for a security camera it would do poorly at…
Reading a license plate seems like precisely the kind of circumstances where spurious 'plausible interpretation' of limited data can cause trouble.
You show in a courtroom a CNN-enhanced low light image of a car and it's there, literally 'clear as day' - the jury will find it pretty compelling. But maybe the data really wasn't there in the original image, and the CNN just filled in some blanks based on previous images of license plates, letters, and just random noise it had seen in the past.
The worry is when these kinds of algorithms get built in to basic image capture processes, so you never even see the raw data, only data that has already been filtered through the inbuilt prejudices of the CNN enhancement suite.
The camera never lies, but now it doesn't have to, because it can convince itself it saw something that wasn't really there...