wenbihan/reproducible-image-denoising-state-of-the-art
A curated benchmark of reproducible state-of-the-art single image denoising algorithms including deep learning and classical methods.
Not currently ranked — collecting fresh signals.
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This repository aggregates popular image denoising works that have publicly available code and demonstrate reproducible state-of-the-art results. It covers classical approaches (NLM, BM3D) and deep learning-based methods (DnCNN, FFDNet) for image denoising, organized by algorithm categories including filtering, sparse coding, and deep learning. The collection serves as a reference for researchers to compare approaches and access implementations.
Frequently asked
- What is wenbihan/reproducible-image-denoising-state-of-the-art?
- A curated benchmark of reproducible state-of-the-art single image denoising algorithms including deep learning and classical methods.
- Is reproducible-image-denoising-state-of-the-art open source?
- Yes — wenbihan/reproducible-image-denoising-state-of-the-art is an open-source project tracked on heatdrop.
- How popular is reproducible-image-denoising-state-of-the-art?
- wenbihan/reproducible-image-denoising-state-of-the-art has 2.5k stars on GitHub.
- Where can I find reproducible-image-denoising-state-of-the-art?
- wenbihan/reproducible-image-denoising-state-of-the-art is on GitHub at https://github.com/wenbihan/reproducible-image-denoising-state-of-the-art.