alt.hn

5/18/2026 at 2:10:47 PM

When Fast Fourier Transform Meets Transformer for Image Restoration (2024)

 https://github.com/deng-ai-lab/SFHformer

by teleforce

5/20/2026 at 6:59:42 PM

Relatedly, Marcin Wichary wrote a nice post about using FFT to remove moiré and halftone effects when scanning images that were printed with halftones.

It's from 2021: Moiré no More (https://newsletter.shifthappens.site/archive/moire-no-more/).

by jongala

5/20/2026 at 10:33:56 PM

I'd like to see a sequel where the fractional fourier transform is used for image restoration

by krackers

5/20/2026 at 2:44:48 PM

There have been some interesting advances in trying to add spectral information to the data that a learning architecture has at its disposal, but there are a couple roadblocks that I don’t think have been solved yet.

1. Complex-valued NNs are not an easy generalization of real ones.

2. A localization in one domain implies non-local behavior in the other (this is the Fourier uncertainty principle).

Fourier Neural Operators (FNOs) come close to what I want to see in this area but since they enforce sparsity in the spectral domain their application is necessarily limited.

by TimorousBestie

5/20/2026 at 3:33:35 PM

I do wonder if a wavelet transform might be better.

by FuckButtons

5/20/2026 at 4:58:19 PM

I think one can do better with a wavelet, shearlet, or curvelet transform that is adapted to the problem domain at hand. But the uncertainty principle still haunts those transforms, and anyway the goal is to be domain-agile.

by TimorousBestie

5/20/2026 at 5:09:17 PM

Was there a conclusion?

by waynecochran

5/20/2026 at 1:48:37 PM

[2024]

by gryfft