51–60 of 155 posts
I used to roll my eyes at crime television shows, whenever they said "Enhance" for a low quality image. Now it seems the possibility of that becoming realistic are increasing with a steady clip, based on this paper and other enhancement techniques I've seen posted here.
Although what we don't have is any certainty that the enhanced face actually looks like the killer.
Earlier quoted context omitted.
What you can do though, in limited circumstances, is create a still picture with more detail from a lower quality video. https://photo.stackexchange.com/questions/17098/csi-image-re...
It's a well-known technique in astronomy, eg https://www.aanda.org/articles/aa/ps/2005/22/aa2320-04.ps.gz
Earlier quoted context omitted.
That's what video compression does now.
No, today's compression is about compressing what's already in the one movie. But imagine that you run your training set over 100's or 1000's of films, and extract just enough to represent say different types of trees in a few bytes. You could 'compress' a film by replacing data with markers that essentially describe some properties of the tree, and those properties + the training set are then used during 'decompress…
So is there an analagous process that would apply to audio I wonder?
There kind of already is audio equivalent: MIDI. It supplies low resolution timing and pitch information and it's up to synthesizer to produce audio output matching those data.
For example an approach similar to https://gauthamzz.github.io/2017/09/23/AudioStyleTransfer/ but then using the methods described in the PixelNN paper.
I used to roll my eyes at crime television shows, whenever they said "Enhance" for a low quality image. Now it seems the possibility of that becoming realistic are increasing with a steady clip, based on this paper and other enhancement techniques I've seen posted here.
Except, and this is really the fundamental catch, it's not so much "enhance" as it is "project a believable substitute/interpretation". You fundamentally can't get back information that has been destroyed/or never captured in the first place. What you can do is fill in the gaps/information with plausible values. I don't know whether this sounds like I'm splitting hairs, but it's really important that the general publ…
I love this cliché. I've seen it thousands of times, and probably written it myself a few times. We all repeat stuff like that ad nauseam, without ever thinking.
Because it's fundamentally flawed, especially in the context that it has usually been applied to, namely criticising the CSI:XYZ trope of "enhancing images".
The truth is that there is a lot more information in a low-res image than meets the eye.
Even if you can't read the letters on a license plate, it can be recovered by an algorithm. If the Empire State Building is in the background, it's likely to be a US license plate. Maybe only some letters would result in the photo's low-res pattern. If you only see part of a letter, knowing the font may allow you to rule out many letters or numbers etc...
It's similar to that guy who used Photoshop's swirl effect to hide his face, not knowing that the effect is deterministic, and can easily be undone.
The error mostly appears to be in assuming that the information has been destroyed, when in reality it's often just obscured. And Neural Nets are excellent in squeezing all the information out noisy data.
I used to roll my eyes at crime television shows, whenever they said "Enhance" for a low quality image. Now it seems the possibility of that becoming realistic are increasing with a steady clip, based on this paper and other enhancement techniques I've seen posted here.
Except, and this is really the fundamental catch, it's not so much "enhance" as it is "project a believable substitute/interpretation". You fundamentally can't get back information that has been destroyed/or never captured in the first place. What you can do is fill in the gaps/information with plausible values. I don't know whether this sounds like I'm splitting hairs, but it's really important that the general publ…
Earlier quoted context omitted.
Except, and this is really the fundamental catch, it's not so much "enhance" as it is "project a believable substitute/interpretation". You fundamentally can't get back information that has been destroyed/or never captured in the first place. What you can do is fill in the gaps/information with plausible values. I don't know whether this sounds like I'm splitting hairs, but it's really important that the general publ…
> Except, and this is really the fundamental catch, it's not so much "enhance" as it is "project a believable substitute/interpretation". I would argue that this is a form of enhancement though, and in some cases will be enough to completely reconstruct the original image. For example, if I give you a scanned PDF, and you know for a fact that it was size 12 black Ariel text on a white background, this can feasibly le…
When humans think about "enhance", they imagine extracting subtle details that were not obvious from the original, which implies that they know very little about what distribution the original image comes from. If they did, they wouldn't have a need for "enhance" 99% of the time -- the remaining 1% is for artistic purposes, which this is indeed suited for.
It'll be interesting to see how society copes with the removal of the "photographs = evidence" prior.
> when enhancing celebrity images, if the model is able to figure out who is in the picture this massively decreases the set of plausible outputs.
This is an excellent insight.
Earlier quoted context omitted.
No, today's compression is about compressing what's already in the one movie. But imagine that you run your training set over 100's or 1000's of films, and extract just enough to represent say different types of trees in a few bytes. You could 'compress' a film by replacing data with markers that essentially describe some properties of the tree, and those properties + the training set are then used during 'decompress…
And some action movies could then be compressed to mere bytes if you basically have a virtual movie studio in your PC.
I used to roll my eyes at crime television shows, whenever they said "Enhance" for a low quality image. Now it seems the possibility of that becoming realistic are increasing with a steady clip, based on this paper and other enhancement techniques I've seen posted here.
It went from this:
https://media.giphy.com/media/pUf3YfamV7BV6/giphy.gif
To this:
http://img.go-here.nl/Roundhay_Garden_Scene.gif
The funniest part was that the resolution really goes up if you make 1 px into 40 and align the frames accurately (then adjust opacity to the level of blur)
The crime television thing would be possible if you have enough frames of the gangster.
Very clever. I wonder if something like this could be used for other forms of sensor data as well?
