</> Code with Papers by Syntology

Papers › Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets...

Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling

13 Jul 2022arXiv:2207.06103archive 2025-07-28

Hansen Feng, Lizhi Wang, Yuzhi Wang, Hua Huang

Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream. However, the limited data volume and complicated noise distribution have constituted a learnability bottleneck for paired real data, which limits the denoising performance of learning-based methods. To address this issue, we present a learnability enhancement strategy to reform paired real data according to noise modeling. Our strategy consists of two efficient techniques: shot noise augmentation (SNA) and dark shading correction (DSC). Through noise model decoupling, SNA improves the precision of data mapping by increasing the data volume and DSC reduces the complexity of data mapping by reducing the noise complexity. Extensive results on the public datasets and real imaging scenarios collectively demonstrate the state-of-the-art performance of our method. Our code is available at: https://github.com/megvii-research/PMN.

PaperPDFCode

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

megvii-research/pmn officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

DenoisingImage Denoising

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Denoising ELD SonyA7S2 x100 PMN PSNR (Raw) 46.50 #2 of 9 Archive leaderboard report
Image Denoising ELD SonyA7S2 x100 PMN SSIM (Raw) 0.985 #2 of 9 Archive leaderboard report
Image Denoising ELD SonyA7S2 x200 PMN PSNR (Raw) 44.51 #2 of 10 Archive leaderboard report
Image Denoising ELD SonyA7S2 x200 PMN SSIM (Raw) 0.973 #2 of 10 Archive leaderboard report
Image Denoising SID SonyA7S2 x100 PMN PSNR (Raw) 43.16 #1 of 5 Archive leaderboard report
Image Denoising SID SonyA7S2 x100 PMN SSIM (Raw) 0.960 #1 of 5 Archive leaderboard report
Image Denoising SID SonyA7S2 x250 PMN PSNR (Raw) 40.92 #2 of 10 Archive leaderboard report
Image Denoising SID SonyA7S2 x250 PMN SSIM (Raw) 0.947 #2 of 10 Archive leaderboard report
Image Denoising SID x100 PMN PSNR (Raw) 43.16 #2 of 8 Archive leaderboard report
Image Denoising SID x100 PMN SSIM 0.960 #2 of 8 Archive leaderboard report
Image Denoising SID x300 PMN PSNR (Raw) 37.77 #2 of 8 Archive leaderboard report
Image Denoising SID x300 PMN SSIM 0.934 #2 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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