5 ms·

sauce?

morsch 6y ago
Literally the first Google result for super-resolution from video: https://paperswithcode.com/task/video-super-resolution https://paperswithcode.com/task/video-super-resolution
- murbard2 6y ago
  Lots of neural-network approaches for this which work quite well but are also likely to learn priors from the data which can introduce tricky issues for police work. There are however approaches which aren't based on machine learning at all, for example https://www.di.ens.fr/~mallat/papiers/whitepaper.pdf https://www.di.ens.fr/~mallat/papiers/whitepaper.pdf
- codetrotter 6y ago
  IMO it’s still nice to provide links. For one, readers won’t know ahead of time if it’ll be the first result or not. Aside from that, search results can vary from person to person what with Google filter bubble. And on top of that, if I find a source I still don’t know if it’s the same thing that was being talked about. Worst case I might find a bad source instead and then disregard the information because the source I found for it was bad while there existed good sources for the information unbeknownst to me.
- _visgean 6y ago
  Yeah thats not a source at all. Its a collection of paper on the relevant topic. The claim of the parent comment was > but super-resolution from video works. It doesn't make up information based on what it expects the face to be, but collects and aggregates information from multiple frames. So I was simply interested in hearing where this claim comes from. Has any of these papers mentioned that this is the case? I briefly went through some of them but did not see any such guarantee.
  - murbard2 6y ago
    Many of the modern ones are based on deep learning and might indeed capture priors for faces. However, other approaches do not depend on machine-learning. The priors that they implicitly use are weak or rather general (e.g. natural images have mostly sparse wavelet representations, videos represent things that move mostly continuously, etc). They essentially compensate for the motion over several frames and use the redundant information to denoise the result.
gchamonlive 6y ago
"sauce"? Like in tomato sauce? What's going on here?
- Kliment 6y ago
  "sauce" is common internet slang for "source"
  - michaelmior 6y ago
    Oh, I assumed it was referring to what secret sauce was used.

sauce?

Literally the first Google result for super-resolution from video: https://paperswithcode.com/task/video-super-resolution https://paperswithcode.com/task/video-super-resolution

murbard2 6y ago
Lots of neural-network approaches for this which work quite well but are also likely to learn priors from the data which can introduce tricky issues for police work. There are however approaches which aren't based on machine learning at all, for example https://www.di.ens.fr/~mallat/papiers/whitepaper.pdf https://www.di.ens.fr/~mallat/papiers/whitepaper.pdf
codetrotter 6y ago
IMO it’s still nice to provide links. For one, readers won’t know ahead of time if it’ll be the first result or not. Aside from that, search results can vary from person to person what with Google filter bubble. And on top of that, if I find a source I still don’t know if it’s the same thing that was being talked about. Worst case I might find a bad source instead and then disregard the information because the source I found for it was bad while there existed good sources for the information unbeknownst to me.
_visgean 6y ago
Yeah thats not a source at all. Its a collection of paper on the relevant topic. The claim of the parent comment was > but super-resolution from video works. It doesn't make up information based on what it expects the face to be, but collects and aggregates information from multiple frames. So I was simply interested in hearing where this claim comes from. Has any of these papers mentioned that this is the case? I briefly went through some of them but did not see any such guarantee.
- murbard2 6y ago
  Many of the modern ones are based on deep learning and might indeed capture priors for faces. However, other approaches do not depend on machine-learning. The priors that they implicitly use are weak or rather general (e.g. natural images have mostly sparse wavelet representations, videos represent things that move mostly continuously, etc). They essentially compensate for the motion over several frames and use the redundant information to denoise the result.

gchamonlive 6y ago

"sauce"? Like in tomato sauce? What's going on here?

Kliment 6y ago
"sauce" is common internet slang for "source"
- michaelmior 6y ago
  Oh, I assumed it was referring to what secret sauce was used.