No code available.
Which is sadly par for the course in this field, or at least my experience. You can always email the group...
PixelNN – Example-Based Image Synthesis
101–110 of 155 posts
Re: PixelNN – Example-Based Image Synthesis
#102Earlier 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…
>we're interpolating or projecting information that is not there But that's not fully accurate either. Sometimes the information in total will really be a more accurate representation of reality than the blurred image. Maybe it could be described as an educated guess, sometimes wrong, sometimes invaluable. It would be interesting to see the results starting with higher quality images. With the camera quality increasi…
>> Maybe it could be described as an educated guess, sometimes wrong, sometimes invaluable.
When is a guess invaluable?
Re: PixelNN – Example-Based Image Synthesis
#103I found the title somewhat misleading. I was expecting some clever application of the nearest-neighbor interpolation. But this seems to involve neural nets and appears far from "simple" to me (I'm not in the image processing field though).
> I was expecting some clever application of the nearest-neighbor interpolation. But this seems to involve neural nets and appears far from "simple" to me (I'm not in the image processing field though).
It's not that far off actually, but they are talking about nearest neighbor Markov chains, not interpolation. You probably already know nearest neighbor Markov chains because there are lots of text examples, and a ton of Twitter bots that are generating random text this way. The famous historical example was the usenet post that said "I spent an interesting evening recently with a grain of salt." https://en.m.wikipedia.org/wiki/Mark_V._Shaney
This paper does use a NN to synthesize an image, which is conceptually pretty simple, even if difficult to implement well. After that they use a nearest neighbor Markov chain to fill in high frequencies. The first paper referenced is also the simplest example: http://graphics.cs.cmu.edu/people/efros/research/EfrosLeung....
That paper fills missing parts of an image using a single example, by using a Markov chain built on the nearest neighboring pixels. That paper is also one of the only image synthesis papers (or perhaps the only paper) that can synthesize readable text from an image of text. That's really cool because the inspiration was text-based Markov chains.
Re: PixelNN – Example-Based Image Synthesis
#104Earlier quoted context omitted.
The term in neural net research is 'face hallucination': https://people.csail.mit.edu/celiu/FaceHallucination/fh.html Take a low resolution input image, and hallucinate a higher resolution version by statistically assembling bits from similar images in a large data set of training images.
If anyone ever tries to use this in court I hope they call it "Face Hallucination" and not "Image Reconstruction". On the research side, I wonder what the point of this is. I find it interesting but of little practical value.
It's a way to refine their models. A systematic model-based representation of data is basically also a generator of that data.
Why is that? Blame Kolmogorov. There are deep connections between compression, serialization, and computation. An optimal compression scheme is a serialization and the Turing-complete program to decode it. For example: you can compress pi into a few lines of algorithm plus a starting constant like 4.
Re: PixelNN – Example-Based Image Synthesis
#105"Enhance" is real. When will this stuff trickle into lower level law enforcement?
Re: PixelNN – Example-Based Image Synthesis
#106Earlier quoted context omitted.
Color comes from the initial neural network step. Since skin color is relatively predictable from facial features (ex: nose width), it should be able to do reasonably well.
Really? With what accuracy? This is the kind of assumption that will get research groups into very deep water... Just imagine the kind of CCTV usage being discussed elsewhere in this thread. But the neural network happens to have a wrong bias towards skin colour...
Using image synthesis at all can't be used for up-rezing CCTV imagery, the output is a fabrication and the researchers have all said so. People imagining bad use cases shouldn't be relied on. ;) If an investigator used this to track down criminals, they are the ones getting into deep water and making assumptions.
Re: PixelNN – Example-Based Image Synthesis
#107No code available.
Which is sadly par for the course in this field, or at least my experience. You can always email the group...
I spent too long trying to get RAISR to work when that paper came out. You can try it out from some Github repos but no one has been able to recreate the results Google presented. I would be hard pressed to say my hires photos looked any better than the originals when scaled up on my iPhone screen.
I do wish they would release the code AND any related training images they used to get those results.
Re: PixelNN – Example-Based Image Synthesis
#108"Enhance" is real. When will this stuff trickle into lower level law enforcement?
Should it? The accuracy of the pictures were dismal.
Re: PixelNN – Example-Based Image Synthesis
#109"Enhance" is real. When will this stuff trickle into lower level law enforcement?
Hopefully never, but I'm sure someone will see this and try!
(Because these kind of techniques aren't really enhancing the images in a way that gives you new and useful information: they are taking the low-res images as input, and giving you a plausible high-res image as output, based on it's training data. It is NOT however trying to say "this is the ACTUAL high res image that generated this low-res image"
Re: PixelNN – Example-Based Image Synthesis
#110I have a large collection of images, many being accessible through google image search. I wonder if there could be a way to "index" those images so I can find them back without storing the whole image, using some type of clever image histogram or hashing-kind function. I wonder if that thing already exist, since there are many images, and since most images have a lot of difference in their data, could it be possible…
That's perceptual hashing. Check out https://www.phash.org/
Is there simpler way to implement it? This is a library, but aren't more common ways to do this?