mlsanityData to 7 Oct 2026

Noise2Self: Blind Denoising by Self-Supervision

arXiv 1901.11365 ↗First posted 30 Jan 2019cs.CV cs.LG stat.MLcited by 274

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Cited by 26 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose a general framework for denoising high-dimensional measurements which requires no prior on the signal, no estimate of the noise, and no clean training data. The only assumption is that the noise exhibits statistical independence across different dimensions of the measurement, while the true signal exhibits some correlation. For a broad class of functions ("J-invariant"), it is then possible to estimate the performance of a denoiser from noisy data alone. This allows us to calibrate J-invariant versions of any parameterised denoising algorithm, from the single hyperparameter of a median filter to the millions of weights of a deep neural network. We demonstrate this on natural image and microscopy data, where we exploit noise independence between pixels, and on single-cell gene expression data, where we exploit independence between detections of individual molecules. This framework generalizes recent work on training neural nets from noisy images and on cross-validation for matrix factorization.

Key facts. Cited by 274 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jan 2019.

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Citing papers per year: 2020: 1; 2021: 35; 2022: 51; 2023: 62; 2024: 44; 2025: 46; 2026: 35.

274 papers.

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  1. 2021NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Imagescited by 336
  2. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  3. 2024A Survey on Diffusion Models for Inverse Problemscited by 173
  4. 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 171
  5. 2021Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Imagescited by 110
  6. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 108
  7. 2022AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Networkcited by 92
  8. 2021Equivariant Imaging: Learning Beyond the Range Spacecited by 83
  9. 2022Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spotscited by 83
  10. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  11. 2023Zero-Shot Noise2Noise: Efficient Image Denoising without any Datacited by 59
  12. 2022C2N: Practical Generative Noise Modeling for Real-World Denoisingcited by 56
38 more
  1. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Modelscited by 50
  2. 2022Dancing under the stars: video denoising in starlightcited by 47
  3. 2023Neural Degradation Representation Learning for All-In-One Image Restorationcited by 46
  4. 2022CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Imagecited by 45
  5. 2024Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Datacited by 44
  6. 2025Is Noise Conditioning Necessary for Denoising Generative Models?cited by 42
  7. 2023Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Datacited by 40
  8. 2023GSURE-Based Diffusion Model Training with Corrupted Datacited by 40
  9. 2023LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoisingcited by 37
  10. 2021IDR: Self-Supervised Image Denoising via Iterative Data Refinementcited by 36
  11. 2023Spatially Adaptive Self-Supervised Learning for Real-World Image Denoisingcited by 33
  12. 2023Random Sub-Samples Generation for Self-Supervised Real Image Denoisingcited by 31
  13. 2023MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Networkcited by 30
  14. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 29
  15. 2024Transfer CLIP for Generalizable Image Denoisingcited by 29
  16. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learningcited by 28
  17. 2021Interpretable Unsupervised Diversity Denoising and Artefact Removalcited by 26
  18. 2026INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrievalcited by 26
  19. 2021Optimal Transport for Unsupervised Denoising Learningcited by 24
  20. 2021FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noisecited by 22
  21. 2022Self-Supervised Representation Learning via Latent Graph Predictioncited by 22
  22. 2022Denoising Masked AutoEncoders Help Robust Classificationcited by 21
  23. 2024An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observationscited by 20
  24. 2021Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matchingcited by 18
  25. 2022Learn from Unpaired Data for Image Restoration: A Variational Bayes Approachcited by 18
  26. 2024How much is a noisy image worth? Data Scaling Laws for Ambient Diffusioncited by 18
  27. 2021Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancementcited by 17
  28. 2022Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Imagescited by 17
  29. 2023Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarioscited by 17
  30. 2023Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensemblingcited by 17
  31. 2023Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Reviewcited by 17
  32. 2023Test-Time Degradation Adaptation for Open-Set Image Restorationcited by 17
  33. 2021Adaptive Denoising via GainTuningcited by 16
  34. 2023PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoisingcited by 16
  35. 2021Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imagingcited by 15
  36. 2023Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoisingcited by 15
  37. 2023Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurringcited by 14
  38. 2022Towards Adversarially Robust Deep Image Denoisingcited by 13

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