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PixelNN – Example-Based Image Synthesis

cs.cmu.edu

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Re: PixelNN – Example-Based Image Synthesis

#11

Post by gambiting »

xyzzy_plugh wrote: post #9
thaw13579 wrote: post #5

Earlier quoted context omitted.

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.

Sure, but if we go by how the police works now, they will take a plate produced by the computer as 100% given and arrest/shoot the owner of that plate because "computer said so".

Re: PixelNN – Example-Based Image Synthesis

#12

Post by ComputerGuru »

gambiting wrote: post #10
ACow_Adonis wrote: post #4

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…

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…

> you only have one value that encodes to a certain hash value, right?

Errr wrong. A perfect hash, yes. But they're never perfect. You have a collision domain and you hope that you don't have enough inputs to trigger a birthday paradox.

Look at the pictures on the article. It's an outline of the shoe. That's your hash. ANY shoe with that general outline resolves to that same hash.

If your input is objects found in the Oxford English Dictionary, you'll have low collisions. An elephant doesn't hash to that outline. But if your inputs is the Kohl's catalog, you'll have an unacceptable collision rate.

Hashes are attempts at creating a _truncated_ "unique" representation of an input. They throw away data they hope isn't necessary to uniquely identify between possible inputs (bits). A perfect hash for all possible 32 bit values is 32 bits. You can't even have a collision free 31 bit hash.

So back to the blurry security camera footage of a license plate or a face. Sure, that "hash" can reliably tell you that it wasn't a sasquatch that committed the robbery, but it literally doesn't contain the data necessary to _ever_ prove it was the suspect in question, even if the techs _can_ prove that the suspect hashes to the image in the footage.

Re: PixelNN – Example-Based Image Synthesis

#14

Post by ACow_Adonis »

gambiting wrote: post #10
ACow_Adonis wrote: post #4

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…

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…

Without getting too technical because I hate typing on a phone, you're technically right in the sense of a theoretical hash.

But in real life there's collisions.

And in real life image or sound compression, blurs, artifacts and resolutions, it is fundamentally destroying information in practice. It is no longer the comparatively difficult but theoretically possible task of reversing a perfect hash, but more like mapping a name to the characters/bucket RXXHXXXX where x could be anything.

There are lots of values we can replace X with which are plausible, but without an outside source of information, we can't know what the real values in the original name was.

Re: PixelNN – Example-Based Image Synthesis

#15

Post by usrusr »

ACow_Adonis wrote: post #4
otp124 wrote: post #3

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…

Yeah, replace the training set with cartoon characters and the crime show dialog goes like this:

"Zoom! Enhance! Zoom! Enhance! Enhance! Oh my god it's full of Smurfs..."

Re: PixelNN – Example-Based Image Synthesis

#16

Post by WillReplyfFood »

jopsen wrote: post #6
ACow_Adonis wrote: post #4

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…

Sometimes US justice system seems very "approximate". So why not convict people based on interpolated evidence? - I'm joking of course :) hehe

You seem like a funny guy- the batmaNN interpolates that you are very likely a joker.

Re: PixelNN – Example-Based Image Synthesis

#19

Post by omtinez »

ZeroGravitas wrote: post #8
ACow_Adonis wrote: post #4

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…

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 huma…

I haven't read the paper in full detail, but reading between the lines I'm guessing that there's a significant portion of manual processing and hand waving involved. From the abstract, emphasis mine:

> the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner.

My interpretation is that they select training data by hand and generate a bunch of outputs. Repeating the process until they like the final result. From the paper:

> we allow a user to have an arbitrarily-fine level of control through on-the-fly editing of the exemplar set (E.g., “resynthesize an image using the eye from this image and the nose from that one”).

Re: PixelNN – Example-Based Image Synthesis

#20

Post by IncRnd »

gambiting wrote: post #10
ACow_Adonis wrote: post #4

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…

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…

That won't work. A lot of people have tried to create systems that they claim always compress movies or files or something else. Yet, none of those systems ever come to market. They get backers to give them cash, then they disappear. The reason they don't come to market is that they don't exist. Look up the pigeon-hole principle. It's the very first principle of data compression.

You can't compress a file by repeatedly storing a series of hashes, then hashes of those hashes, down into smaller and smaller representations. The reason that you cannot do this is that you cannot create a lossless file smaller than the original entropy. If you could happen to do so, however, you would get down to ever smaller files, until you had one byte left. But, you could never decompress such a file, because there is no single correct interpretation of such a decompression. In other words, your decompression is not the original file.

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