mlsanityData to 14 Oct 2026

Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

arXiv 1906.00651 ↗First posted 3 Jun 2019eess.IV cs.CVcited by 51

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Abstract (arXiv)

Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practical applications. This motivates self-supervised training methods such as Noise2Void~(N2V) that operate on single noisy images. Self-supervised methods are, unfortunately, not competitive with models trained on image pairs. Here, we present 'Probabilistic Noise2Void' (PN2V), a method to train CNNs to predict per-pixel intensity distributions. Combining these with a suitable description of the noise, we obtain a complete probabilistic model for the noisy observations and true signal in every pixel. We evaluate PN2V on publicly available microscopy datasets, under a broad range of noise regimes, and achieve competitive results with respect to supervised state-of-the-art methods.

Key facts. Cited by 51 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Jun 2019.

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Citing papers per year: 2021: 9; 2022: 13; 2023: 13; 2024: 6; 2025: 4; 2026: 6.

51 papers.

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  1. 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 173
  2. 2022AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Networkcited by 93
  3. 2022Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spotscited by 84
  4. 2023Zero-Shot Noise2Noise: Efficient Image Denoising without any Datacited by 61
  5. 2022Dancing under the stars: video denoising in starlightcited by 47
  6. 2023LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoisingcited by 37
  7. 2023Spatially Adaptive Self-Supervised Learning for Real-World Image Denoisingcited by 33
  8. 2023On the Posterior Distribution in Denoising: Application to Uncertainty Quantificationcited by 33
  9. 2023MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Networkcited by 30
  10. 2021Interpretable Unsupervised Diversity Denoising and Artefact Removalcited by 26
  11. 2021Optimal Transport for Unsupervised Denoising Learningcited by 24
  12. 2021Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancementcited by 17
38 more
  1. 2023Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Reviewcited by 17
  2. 2021Adaptive Denoising via GainTuningcited by 16
  3. 2022Self-supervised learning of hologram reconstruction using physics consistencycited by 14
  4. 2023Self-Supervised Image Denoising for Real-World Images with Context-aware Transformercited by 13
  5. 2021Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoisercited by 10
  6. 2021Deformation-Compensated Learning for Image Reconstruction without Ground Truthcited by 9
  7. 2024Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoisingcited by 9
  8. 2022N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecturecited by 7
  9. 2024Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoisingcited by 6
  10. 2025Rotation-Equivariant Self-Supervised Method in Image Denoisingcited by 6
  11. 2022Patch-Craft Self-Supervised Training for Correlated Image Denoisingcited by 5
  12. 2022μSplit: efficient image decomposition for microscopy datacited by 5
  13. 2026Self-Supervised Learning from Noisy and Incomplete Datacited by 5
  14. 2023Unsupervised Image Denoising with Score Functioncited by 4
  15. 2024Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoisingcited by 4
  16. 2022Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?cited by 3
  17. 2023Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment Losscited by 3
  18. 2024denoiSplit: a method for joint microscopy image splitting and unsupervised denoisingcited by 3
  19. 2025HazeMatching: Dehazing Light Microscopy Images with Guided Conditional Flow Matchingcited by 3
  20. 2021Joint self-supervised blind denoising and noise estimationcited by 2
  21. 2021View Blind-spot as Inpainting: Self-Supervised Denoising with Mask Guided Residual Convolutioncited by 2
  22. 2022Deep learning based domain adaptation for mitochondria segmentation on EM volumescited by 2
  23. 2022Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Datacited by 2
  24. 2022Image quality measurements and denoising using Fourier Ring Correlationscited by 1
  25. 2022Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imagingcited by 1
  26. 2023Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noisecited by 1
  27. 2024On normalization-equivariance properties of supervised and unsupervised denoising methods: a surveycited by 1
  28. 2025Unsupervised Real-World Denoising: Sparsity is All You Needcited by 1
  29. 2026λSplit: Self-Supervised Content-Aware Spectral Unmixing for Fluorescence Microscopycited by 1
  30. 2022Speckle Image Restoration without Clean Datacited by 0
  31. 2023Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learningcited by 0
  32. 2023Self-Supervised Single-Image Deconvolution with Siamese Neural Networkscited by 0
  33. 2023Blind Biological Sequence Denoising with Self-Supervised Set Learningcited by 0
  34. 2024SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensorscited by 0
  35. 2025QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problemscited by 0
  36. 2026Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Imagescited by 0
  37. 2026Distilling Vision Transformers for Distortion-Robust Representation Learningcited by 0
  38. 2026Deploying Self-Supervised Learning for Real Seismic Data Denoisingcited by 0

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