Datasets › Nam
Nam (A holistic approach to cross-channel image noise modeling and its application to image denoising)
A holistic approach to cross-channel image noise modeling and its application to image denoising
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Image Denoising | Nam | PNGAN PSNR 40.78 | Learning to Generate Realistic Noisy Images via... | caiyuanhao1998/PNGAN +1 | 1 | Compare |
Papers archive 2025-07-28
1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 18. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training | 2 | 1 | 6 Apr 2022 | ran 14 of 18 samples (4 unverified) |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
https://github.com/woozzu/ccnoise
Modalities archive 2025-07-28
No modality tagged.
Languages archive 2025-07-28
Variants archive 2025-07-28
- Nam
1 variant name, as the archive lists them.
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