Methods › Computer Vision › Image Denoising Models › Noise2Fast
Noise2Fast
Introduced by Jason Lequyer et al. in Noise2Fast: Fast Self-Supervised Single Image Blind Denoising
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Noise2Fast is a model for single image blind denoising. It is similar to masking based methods -- filling in the pixel gaps -- in that the network is blind to many of the input pixels during training. The method is inspired by Neighbor2Neighbor, where the neural network learns a mapping between adjacent pixels. Noise2Fast is tuned to speed by using a discrete four image training set obtained by a form of downsampling called “checkerboard downsampling.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation 6 Apr 2024 · 0 repositories · arXiv:2404.04726
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Noise2Fast: Fast Self-Supervised Single Image Blind Denoising 23 Aug 2021 · 1 repository · arXiv:2108.10209Syntology ran 0 of 1 samples · 1 unverified
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Denoising | 2 |
| Image Denoising | 2 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections