mlsanityData to 14 Oct 2026

Noise2Void - Learning Denoising from Single Noisy Images

arXiv 1811.10980 ↗First posted 27 Nov 2018cs.CVcited by 364

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

Abstract (arXiv)

The field of image denoising is currently dominated by discriminative deep learning methods that are trained on pairs of noisy input and clean target images. Recently it has been shown that such methods can also be trained without clean targets. Instead, independent pairs of noisy images can be used, in an approach known as Noise2Noise (N2N). Here, we introduce Noise2Void (N2V), a training scheme that takes this idea one step further. It does not require noisy image pairs, nor clean target images. Consequently, N2V allows us to train directly on the body of data to be denoised and can therefore be applied when other methods cannot. Especially interesting is the application to biomedical image data, where the acquisition of training targets, clean or noisy, is frequently not possible. We compare the performance of N2V to approaches that have either clean target images and/or noisy image pairs available. Intuitively, N2V cannot be expected to outperform methods that have more information available during training. Still, we observe that the denoising performance of Noise2Void drops in moderation and compares favorably to training-free denoising methods.

Key facts. Cited by 364 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2018.

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Citing papers per year: 2020: 1; 2021: 46; 2022: 58; 2023: 75; 2024: 59; 2025: 62; 2026: 63.

364 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 180
  4. 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 173
  5. 2021Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Imagescited by 113
  6. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 111
  7. 2022AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Networkcited by 93
  8. 2022Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesiscited by 86
  9. 2022Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spotscited by 84
  10. 2021Equivariant Imaging: Learning Beyond the Range Spacecited by 83
  11. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 77
  12. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
38 more
  1. 2021Physics-based Noise Modeling for Extreme Low-light Photographycited by 67
  2. 2022MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusioncited by 67
  3. 2023Zero-Shot Noise2Noise: Efficient Image Denoising without any Datacited by 61
  4. 2023Masked Image Training for Generalizable Deep Image Denoisingcited by 57
  5. 2022C2N: Practical Generative Noise Modeling for Real-World Denoisingcited by 56
  6. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Modelscited by 51
  7. 2022Dancing under the stars: video denoising in starlightcited by 47
  8. 2023Neural Degradation Representation Learning for All-In-One Image Restorationcited by 46
  9. 2022CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Imagecited by 45
  10. 2024Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Datacited by 44
  11. 2023LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoisingcited by 37
  12. 2021IDR: Self-Supervised Image Denoising via Iterative Data Refinementcited by 36
  13. 2023Spatially Adaptive Self-Supervised Learning for Real-World Image Denoisingcited by 33
  14. 2023On the Posterior Distribution in Denoising: Application to Uncertainty Quantificationcited by 33
  15. 2024Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Datacited by 32
  16. 2023Random Sub-Samples Generation for Self-Supervised Real Image Denoisingcited by 31
  17. 2021DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flowscited by 30
  18. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 30
  19. 2023MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Networkcited by 30
  20. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learningcited by 29
  21. 2023Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branchescited by 29
  22. 2024Transfer CLIP for Generalizable Image Denoisingcited by 29
  23. 2021Interpretable Unsupervised Diversity Denoising and Artefact Removalcited by 26
  24. 2021Optimal Transport for Unsupervised Denoising Learningcited by 24
  25. 2021FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noisecited by 22
  26. 2023A Survey on the Robustness of Computer Vision Models against Common Corruptionscited by 21
  27. 2023From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentationcited by 20
  28. 2023DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Priorcited by 19
  29. 2021Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matchingcited by 18
  30. 2022Learn from Unpaired Data for Image Restoration: A Variational Bayes Approachcited by 18
  31. 2024How much is a noisy image worth? Data Scaling Laws for Ambient Diffusioncited by 18
  32. 2021Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancementcited by 17
  33. 2022Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Imagescited by 17
  34. 2022Self-Supervised Image Restoration with Blurry and Noisy Pairscited by 17
  35. 2023Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarioscited by 17
  36. 2023Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensemblingcited by 17
  37. 2023Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Reviewcited by 17
  38. 2023Test-Time Degradation Adaptation for Open-Set Image Restorationcited by 17

50 most cited of 364 All 364 (JSON)