Pixel Recursive Super Resolution
arxiv.org
arxiv.org
This tech has been around for several years, and some variation was presented in concert with the Boston Marathon investigation. https://arstechnica.com/information-technology/2013/05/hallu...
(Not clear if this was used as part of the investigation, or if it could be used for future investigations)
In terms of the danger of this kind of image confabulation, it seems similar to the block-based compression on Xerox photocopiers which sometimes changed numbers in scanned documents: http://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_...
It would be absolutely wrong use, as it substitutes some information that the software has learned before and combined instead of the non-existing one in the original pixels.
If the software was trained with the picture of the innocent person, it would produce it instead of the picture of the really guilty person. It can only guess and only guess based on with what it was trained with.
Maybe there could be a moral obligation for the designers of the training set: trained with the right data, if you say "enhance" once too often the model might overfit into a scene from Star Trek ("after shooting, video footage shows man in command uniform firing at random redshirts, people in medical uniforms miraculously unharmed").
It's also nice to just add a little extra quality to older... imagery. Works surprisingly well for non-2D stuff too.
https://arxiv.org/abs/1606.01299
https://research.googleblog.com/2016/11/enhance-raisr-sharp-...
>Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the Single Image Super-Resolution (SISR) problem. The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e. a mapping) that when applied to given image that is not in the training set, will produce a higher resolution version of it, where the learning is preferably low complexity. In our proposed approach, the run-time is more than one to two orders of magnitude faster than the best competing methods currently available, while producing results comparable or better than state-of-the-art.
>A closely related topic is image sharpening and contrast enhancement, i.e., improving the visual quality of a blurry image by amplifying the underlying details (a wide range of frequencies). Our approach additionally includes an extremely efficient way to produce an image that is significantly sharper than the input blurry one, without introducing artifacts such as halos and noise amplification. We illustrate how this effective sharpening algorithm, in addition to being of independent interest, can be used as a pre-processing step to induce the learning of more effective upscaling filters with built-in sharpening and contrast enhancement effect.
Do any datasets even exist where the images are at sensor pixel level?
That way the model would 'know' about imaging effects (I can't think of any specifically mechanical effects that could be in play here right this second) etc?
Or am I way off base here....
In fact I wonder if any imaging sensor vendors run R&D trying to come up with novel neural net based debayering approaches - this could be a cheap way of bumping image quality/perceived resolution.
(sorry couldn't help myself)
Given the Background, and the leave texture + alpha, instead of rasterizing, anti-aliazing and then using z-baked lightsources and probe reflections to light it semi-correctly, what would a neural net implementation look like?
Would you even notice the mistakes in a constant flickering scenery like this?
http://www.livescience.com/49929-faulty-forensic-science-fai...
https://ncforensics.wordpress.com/2013/03/04/thousands-of-ca...
Here are some manual techniques people currently use to hunt signals on SDR.[1] A lot of what they do is visual, and enhanced visual fidelity of potential signals would definitely be a big help, if it worked.
Intel doesn't make GPUs.