mlsanityData to 14 Oct 2026

Noise2Noise: Learning Image Restoration without Clean Data

arXiv 1803.04189 ↗First posted 12 Mar 2018cs.CV cs.LG stat.MLcited by 533

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Cited by 47 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption. In practice, we show that a single model learns photographic noise removal, denoising synthetic Monte Carlo images, and reconstruction of undersampled MRI scans -- all corrupted by different processes -- based on noisy data only.

Key facts. Cited by 533 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Mar 2018.

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Citing papers per year: 2020: 1; 2021: 70; 2022: 91; 2023: 107; 2024: 94; 2025: 88; 2026: 82.

533 papers.

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  1. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  3. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
  4. 2021NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Imagescited by 336
  5. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 273
  6. 2024A Survey on Diffusion Models for Inverse Problemscited by 180
  7. 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 173
  8. 2022NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)cited by 158
  9. 2021Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Imagescited by 113
  10. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 111
  11. 2022AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Networkcited by 93
  12. 2021Real-time Neural Radiance Caching for Path Tracingcited by 91
38 more
  1. 2022Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spotscited by 84
  2. 2021Equivariant Imaging: Learning Beyond the Range Spacecited by 83
  3. 2021Input Similarity from the Neural Network Perspectivecited by 79
  4. 2024Complexity Experts are Task-Discriminative Learners for Any Image Restorationcited by 77
  5. 2022Neural Dual Contouringcited by 71
  6. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  7. 2021Physics-based Noise Modeling for Extreme Low-light Photographycited by 67
  8. 2022MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusioncited by 67
  9. 2021Rethinking Noise Synthesis and Modeling in Raw Denoisingcited by 62
  10. 2023Zero-Shot Noise2Noise: Efficient Image Denoising without any Datacited by 61
  11. 2023AI-Generated Content (AIGC): A Surveycited by 59
  12. 2021Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurementscited by 56
  13. 2022C2N: Practical Generative Noise Modeling for Real-World Denoisingcited by 56
  14. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalizationcited by 52
  15. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Modelscited by 51
  16. 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 50
  17. 2022Dancing under the stars: video denoising in starlightcited by 47
  18. 2021Deep Learning for HDR Imaging: State-of-the-Art and Future Trendscited by 45
  19. 2024Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Datacited by 44
  20. 2023Prompt-In-Prompt Learning for Universal Image Restorationcited by 43
  21. 2024RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Modelscited by 43
  22. 2022Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illuminationcited by 41
  23. 2023Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Datacited by 40
  24. 2023GSURE-Based Diffusion Model Training with Corrupted Datacited by 40
  25. 2021Rethinking Deep Image Prior for Denoisingcited by 38
  26. 2023LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoisingcited by 37
  27. 2021IDR: Self-Supervised Image Denoising via Iterative Data Refinementcited by 36
  28. 2022Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstructioncited by 36
  29. 2023Spatially Adaptive Self-Supervised Learning for Real-World Image Denoisingcited by 33
  30. 2024Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Datacited by 32
  31. 2023Random Sub-Samples Generation for Self-Supervised Real Image Denoisingcited by 31
  32. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 30
  33. 2023MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Networkcited by 30
  34. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learningcited by 29
  35. 2023ExposureDiffusion: Learning to Expose for Low-light Image Enhancementcited by 29
  36. 2021Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoderscited by 28
  37. 2021Learning to Drop Points for LiDAR Scan Synthesiscited by 27
  38. 2023Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mappingcited by 27

50 most cited of 533 All 533 (JSON)