| ▲ | When Fast Fourier Transform Meets Transformer for Image Restoration (2024)(github.com) | |||||||
| 49 points by teleforce 2 days ago | 4 comments | ||||||||
| ▲ | sorenjan 2 hours ago | parent | next [-] | |||||||
See also: CosAE: Learnable Fourier Series for Image Restoration (2024) | ||||||||
| ▲ | TimorousBestie 2 hours ago | parent | prev | next [-] | |||||||
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. | ||||||||
| ||||||||
| ▲ | gryfft 3 hours ago | parent | prev [-] | |||||||
[2024] | ||||||||