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An error function against neighboring pixels alone probably would not be enough to reconstruct the image (there are information theoretic limitations on how muc
by Xcelerate 10y ago
An error function against neighboring pixels alone probably would not be enough to reconstruct the image (there are information theoretic limitations on how much noise can exist for a certain model before ground truth recovery becomes impossible); however, depending on the type of image, additional information could be gleaned from images that fall into the same category.
For instance, if I assign each pixel a random color and call that my "image", and then scramble all of the pixels, it is easy to see that recovery of the original image is mathematically impossible. However, if I use a photograph of a person standing in front of a car, information gleaned from similar types of photos could be sufficient to allow complete recovery given only the exact distribution of pixel colors. I don't know whether this is true, but I wouldn't be surprised if it is. (Actually, this seems like a fun hobby project. I may give it a shot.)
- inimino 10y agoI can see making a heuristic assessment that says "based on the blues and greens there is probably a large expanse of sky and some trees in the original image" but getting from there to where the trees are and putting the leaves back on the trees in the right places, I can't see that being even mathematically possible. It's a million-piece jigsaw puzzle where each piece is square and monochromatic. If enhancing a low-resolution image to regain lost information is basically impossible, getting back a picture from a color histogram would surely be much more impossible.