These results look incredible, and with an inference time of only 36 ms for a 4X super resolution on a V100.

No code has been released though?

  Open access to data and code
  Question: Does the paper provide open access to the data and code, with sufficient instruc-
  tions to faithfully reproduce the main experimental results, as described in supplemental
  material?
  Answer: [No]
  Justification: Although we have answered “No” for now, we intend to release the code and
  models to enable the reproducibility of our main experimental results, pending approval
  from the legal department. This temporary status reflects our commitment to open access
  once all necessary permissions are secured.

I've been dabbling in using Fourier analysis in deep learning lately, and I'm surprised it that I haven't turned up very much research in this area (Fourier Neural Operators being what seems to be the biggest exception). Fourier analysis is such a ubiquitous tool, intuitively I'd think it would work great for deep learning. My suspicion has been that complex numbers are difficult to work with, and maybe I'm just bad at surfacing the relevant research, but I'd be interested to hear from those better informed. (My naive approach has been to simply concatenate the real and complex components together into an n+1 dimensional tensor, but surely there's a way that better respects the structure of complex numbers.)

Autoencoders are catching up. Next: luminosity separated from color and UCS.

Been messing with this stuff too so I get the struggle. Cool results but man, waiting on code drops always drives me nuts.