So is there an analagous process that would apply to audio I wonder?
What would the lo-res starting point be? Low sample-rate, bit depth, ...?
I was thinking like a recording of extremely muffled voices.
PixelNN – Example-Based Image Synthesis
151–155 of 155 posts
Re: PixelNN – Example-Based Image Synthesis
#152Earlier 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". 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…
> The catch is that you need to know that the target image comes from roughly the same distribution as the training set. 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%…
Do you think knowing which state the license plate is from is enough prior knowledge?
Re: PixelNN – Example-Based Image Synthesis
#153Earlier quoted context omitted.
Hopefully never. This does not enhance the image - it makes up a plausible imaginary image. EDIT: Furthermore the range of plausible imaginary images that match a given input is high (infinite?).
Why not? A recreation that leads to an identification should be enough for a warrant that could be used for a continued investigation.
We could also just pick a random person off the street and punish them - it would be similarly accurate and fair (actually probably fairer - if this is trained on pictures with a certain bias it will return pictures with that bias).
This paper does not demonstrate an enhancement technique but a phenomena which those using inverse methods called "overfitting".
Re: PixelNN – Example-Based Image Synthesis
#154I 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…
Exactly, this may be possible: [0] but only of the NN has seen such images before, the output will match the training data but says nothing about reality.
[0] https://i.pinimg.com/originals/b5/29/1b/b5291bba7250abd12010...
Re: PixelNN – Example-Based Image Synthesis
#155I 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.
The low resolution to high resolution image synthesis reminds me of the unblur tool that Adobe demoed during Adobe MAX in 2011. Here is the relevant clip if you're interested https://www.youtube.com/watch?v=xxjiQoTp864
That demo was quite impressive, but the technique is completely different. Adobe uses deconvolution to recover information and details that are actually in the picture, but not visible (unintuitively blurring is a mathematically reversible transformation. If you know the characteristics of the blur, then you can reverse it. In fact most of Adobe demo's magic comes from knowing the blur kernel and path in advance, not sure how it works in practice for real photos). But the Neural net demoed in this post just "makes up" the missing info using examples from photos it learned from, there is no information recovery.