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Found 3 papers, 3 papers with code
Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising
Specifically, we integrate physical priors into neural proxies and introduce three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution loss (DDL).
Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding.
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling
Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream.
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Image Denoising
on SID SonyA7S2 x100
