Something to keep in mind is that when upscaling, you are actually inventing (fabricating) detail. Tools like the one presented here are content to invent detail that looks pleasing to the eye, but if you tried to do something like this for photographs you wouldn't get anything that would hold up as evidence. You also wouldn't want to use this to guide a police sketch, because the "enhanced" image actually contains false information compared to the original.
This upscaling can be considered a form of lossy compression. The pixelated images, while ugly, contain more information - the process cannot be reversed due to this loss of information.
> You also wouldn't want to use this to guide a police sketch...
You would, could and can. Take a look at police sketch software, it is a very manual version of this process. There are a very limited number of potential variations of the human face, that is why eigenfaces work for facial recognition. Consider the scenario where you have a photo of a human face, where one half is occluded. In manually reconstructing the image, you wouldn't place the reconstructed eye an inch above the chin - because human faces don't work that way. Neural networks pick up on that. There is software that tailors use in fitting suits, where a few measurements (like weight and arm length) can be used to extrapolate the rest of the measurements (like chest size and torso length). This works because of the limited number of potential human dimensions.
As far as use of these techniques for evidence... I'd actually prefer it to reliance on eye witness accounts, as the algos are open to exact measurement - unlike most of the other stuff that passes for "criminal science" (humans are still in the loop for fingerprint analysis, wtf?).
Yes, I'm not trying to say that software is useless for assisting in approximating detail for facial recognition, but software like this, where in goes a single image and out pops a single "clean and enhanced" image, with no manual guidance in between, sounds like it would be fantastically misleading. Somehow you have to express to the decision-makers (investigators, prosecutors, jury) that there is error and guesswork involved in this process, lest you end up with techno-magic like polygraph tests that are popularly understood to produce evidence that they really don't.
The "manual guidance in between" is where CSI is so incredibly screwed up though (toolmark and fingerprint examiners, polygraph operators, etc). The only criminal science that is actually reliable has cut humans out of the loop (dna, forensic document analysis, computer forensics). Even with the reliable methods, they are still probabilistic, which is exactly how the software we are discussing would work. As far as misleading decision-makers, well that is a more fundamental problem with the justice system... we really need to cut as much human judgement out of the process as possible. I'm looking forward to the day when speech recognition and language parsing are solved problems, because formal logic will fix this situation pretty quickly.
It's great to cut out humans from the loop where we can, but we cannot do so here. As you say, upscaling is lossy (de)compression, and no amount of math is going to reveal information that fundamentally does not exist in a source image. Furthermore, neural networks are trivially fooled: http://news.cornell.edu/stories/2015/03/images-fool-computer... . I'd actually trust a trained neural network far less than a human, just like I'd trust the upscaling technique in this article far less than a human artist. Speed and automation are their advantages compared to trained humans, not quality.
As are eye witness accounts, which have been demonstrated to be pretty useless.
As are fingerprints, a tiny sliver of (maybe?[0]) uniquely identifiable information.
As are autopsies, where the state of the corpse is maintained only in whatever the examiner writes down, x-rays, or snaps a polaroid of.
As are bite marks...
So you've got all that, plus your lawyer's sweaty appeals to emotion in a group of 12 people - of whom four will express a belief in haunted houses and two will claim to have actually seen a ghost [1]. You'd prefer that over an application of math that can be challenged and rationally discussed?
> Furthermore, neural networks are trivially fooled...
A neural network was fooled with the equivalent of a hash collision, one guess as to how to fix that :)
> I'd actually trust a trained neural network far less than a human...
I can't think of a single person I'd trust over math, once maybe Bill Cosby - but not anymore.
> Speed and automation are their advantages compared to trained humans, not quality.
Well in this context I'd say that impartiality and repeatability are pretty important, which are characteristics more likely to describe a math model than an individual with the qualifications of a mailing address - and all the training that can be packing into a 20 minute vhs about civic duty played on a wheeled TV.
