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A unified framework of non-local parametric methods for image denoising

arXiv 2402.13816 ↗First posted 21 Feb 2024cs.CVcited by 0

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Abstract (arXiv)

We propose a unified view of non-local methods for single-image denoising, for which BM3D is the most popular representative, that operate by gathering noisy patches together according to their similarities in order to process them collaboratively. Our general estimation framework is based on the minimization of the quadratic risk, which is approximated in two steps, and adapts to photon and electronic noises. Relying on unbiased risk estimation (URE) for the first step and on ``internal adaptation'', a concept borrowed from deep learning theory, for the second, we show that our approach enables to reinterpret and reconcile previous state-of-the-art non-local methods. Within this framework, we propose a novel denoiser called NL-Ridge that exploits linear combinations of patches. While conceptually simpler, we show that NL-Ridge can outperform well-established state-of-the-art single-image denoisers.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 60 references that are in the map. First posted on arXiv 21 Feb 2024.

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24 of 60 references.

References · 24

  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  2. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
  3. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  4. 2017Deep Image Priorcited by 1,098
  5. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 533
  6. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 510
  7. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 470
  8. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 364
  9. 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 276
  10. 2017Benchmarking Denoising Algorithms with Real Photographscited by 209
  11. 2015Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restorationcited by 195
  12. 2018Multi-level Wavelet-CNN for Image Restorationcited by 184
12 more
  1. 2019High-Quality Self-Supervised Deep Image Denoisingcited by 161
  2. 2018Non-Local Recurrent Network for Image Restorationcited by 160
  3. 2019Noisier2Noise: Learning to Denoise from Unpaired Noisy Datacited by 126
  4. 2018Neural Nearest Neighbors Networkscited by 98
  5. 2022Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesiscited by 86
  6. 2023Zero-Shot Noise2Noise: Efficient Image Denoising without any Datacited by 61
  7. 2018A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoisingcited by 42
  8. 2021Rethinking Deep Image Prior for Denoisingcited by 38
  9. 2021Noise2Fast: Fast Self-Supervised Single Image Blind Denoisingcited by 28
  10. 2023Normalization-Equivariant Neural Networks with Application to Image Denoisingcited by 11
  11. 2021DCT2net: an interpretable shallow CNN for image denoisingcited by 6
  12. 2022Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approachcited by 5

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