Decay2Distill: Leveraging spatial perturbation and regularization for self-supervised image denoising
Not on the map. Why?
Abstract (arXiv)
Unpaired image denoising has achieved promising development over the last few years. Regardless of the performance, methods tend to heavily rely on underlying noise properties or any assumption which is not always practical. Alternatively, if we can ground the problem from a structural perspective rather than noise statistics, we can achieve a more robust solution. with such motivation, we propose a self-supervised denoising scheme that is unpaired and relies on spatial degradation followed by a regularized refinement. Our method shows considerable improvement over previous methods and exhibited consistent performance over different data domains.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 3 Aug 2022.
Builds on
13 of 21 references.
References · 13
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
- 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
- 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 509
- 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
- 2018Toward Convolutional Blind Denoising of Real Photographscited by 274
- 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 274
- 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 171
- 2019Noisier2Noise: Learning to Denoise from Unpaired Noisy Datacited by 125
- 2022Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spotscited by 83
- 2020Noise2Same: Optimizing A Self-Supervised Bound for Image Denoisingcited by 56
- 2018Training Deep Learning Based Denoisers without Ground Truth Datacited by 43
- 2019Deep K-SVD Denoisingcited by 26
Built on by
Not cited yet.