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Learnability Enhancement for Low-light Raw Denoising
- Source :
- Proceedings of the 30th ACM International Conference on Multimedia.
- Publication Year :
- 2022
- Publisher :
- ACM, 2022.
-
Abstract
- 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.<br />Comment: 9 pages, 8 figures, to be published in ACMMM 2022
- Subjects :
- FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV)
Image and Video Processing (eess.IV)
Computer Science - Computer Vision and Pattern Recognition
FOS: Electrical engineering, electronic engineering, information engineering
Electrical Engineering and Systems Science - Image and Video Processing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 30th ACM International Conference on Multimedia
- Accession number :
- edsair.doi.dedup.....59023ab3751d36d3d2f80cd0373277f7