On Image Enhancement

Axis recently ran a piece about how

71% of Canadians believe grainy security video can be enhanced in a lab

A while back I was approached by a company that had some surveillance footage that they hoped I could improve. This was a fraud case, and while there was no issues identifying the suspect (access control logs also helped), it was impossible to determine what he was carrying as he left the building. The footage was low resolution, black and white, heavily compressed and shot outdoor at night. When “enhancing” footage, you have to be careful that you are not “manipulating” the footage, prodding the result in a certain desired direction. In the end, all I could do was to remove a bit of noise, normalize the contrast and brightness, and that was it.

Enhancing is possible, but probably not to the extent that people might believe. One of the more impressive feats is removal of blur – caused by either lack focus or motion blur. To the lay-man there’s not much difference between the kind of blur that you see when you upscale an image, and out of focus blur, however one type of blur can be enhanced, the other can’t. (or can it?)


The same applies if you have an object in the distance covering, say 32 x 64 pixels. If you crop this area and upscale it 10x (using bilinear filtering), you’ll get a blurry 320 x 640. Even if the full source image was 29MP, there’s no way to magically turn the 32 x 64 crop into a 320 x 640 razor sharp image (like you often see in the movies). What counts is the number of real, captured pixels.

The example of single image super resolution works with images where the source image already have a substantial amount of detail to allow the algorithm to work. In almost all examples you can actually identify a person, or read the letters in the source image already. Further enhancement is hardly necessary for ID purposes.

Just something to be aware of.



Author: prescienta

Prescientas ruler

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