PixelNN – Example-Based Image Synthesis
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PixelNN – Example-Based Image Synthesis
1–10 of 155 posts
Re: PixelNN – Example-Based Image Synthesis
#2Thanks for sharing! Very interesting!
Re: PixelNN – Example-Based Image Synthesis
#3I 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.
Re: PixelNN – Example-Based Image Synthesis
#4I 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 public not think we're extracting information in these procedures, we're interpolating or projecting information that is not there.
Very useful for artificially generating skins for each shoe on a shoe rack in a computer game or simulation, potentially disastrous if the general public starts to think it's applicable to security camera footage or admissible as evidence...
Re: PixelNN – Example-Based Image Synthesis
#5I 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.
Approaches like these are hallucinating the high resolution images though--not something that we'd ever want being used for police work. That said, I wonder if it would perform better than eyewitness testimony...
Re: PixelNN – Example-Based Image Synthesis
#6I 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…
Sometimes US justice system seems very "approximate". So why not convict people based on interpolated evidence?
- I'm joking of course :) hehe
Re: PixelNN – Example-Based Image Synthesis
#7So is there an analagous process that would apply to audio I wonder?
Re: PixelNN – Example-Based Image Synthesis
#8I 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…
To give specific examples from their test data, it added stubble to people who didn't have stubble, gave them a different shape of glasses, changed the color of cats, changed the color and brand of sport shoe.
And even then, I'm a little suspicious of how close some of the images got to original without being given color information.
It appears that info was either hidden in the original in a way not apparent to humans or was implicit in their data set in some way that would make it fail on photos of people with different skin tones.
Re: PixelNN – Example-Based Image Synthesis
#9I 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.
Approaches like these are hallucinating the high resolution images though--not something that we'd ever want being used for police work. That said, I wonder if it would perform better than eyewitness testimony...
You could e.g. ostensibly produce valid license plates, which could be further reduced by matching the car color and model, to produce a small set of calid records.
Re: PixelNN – Example-Based Image Synthesis
#10I 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…
No, but think of these blurred images as a "hash" - in an ideal situation, you only have one value that encodes to a certain hash value, right? So If you are given a hash X you technically can work out that it was derived from value Y - you're not getting back information that was lost - in a way it was merely encoded into the blurred image, and it should be possible to produce a real image which, when blurred, will match what you have.
Don't get me wrong, I think we're still far far far off situation where we can get those reliably, but I can see how you could get the actual face out of a blurred image.