mlsanityData to 7 Oct 2026

Masked and Shuffled Blind Spot Denoising for Real-World Images

arXiv 2404.09389 ↗First posted 15 Apr 2024cs.CV cs.LGcited by 12

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Abstract (arXiv)

We introduce a novel approach to single image denoising based on the Blind Spot Denoising principle, which we call MAsked and SHuffled Blind Spot Denoising (MASH). We focus on the case of correlated noise, which often plagues real images. MASH is the result of a careful analysis to determine the relationships between the level of blindness (masking) of the input and the (unknown) noise correlation. Moreover, we introduce a shuffling technique to weaken the local correlation of noise, which in turn yields an additional denoising performance improvement. We evaluate MASH via extensive experiments on real-world noisy image datasets. We demonstrate on par or better results compared to existing self-supervised denoising methods.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 15 Apr 2024.

Builds on

16 of 34 references.

References · 16

  1. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  2. 2017Deep Image Priorcited by 1,097
  3. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  4. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
  5. 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 171
  6. 2022AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Networkcited by 92
  7. 2022Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spotscited by 83
  8. 2018A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Imagescited by 70
  9. 2020Noise2Same: Optimizing A Self-Supervised Bound for Image Denoisingcited by 56
  10. 2022C2N: Practical Generative Noise Modeling for Real-World Denoisingcited by 56
  11. 2019When AWGN-based Denoiser Meets Real Noisescited by 53
  12. 2022CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Imagecited by 45
4 more
  1. 2023Spatially Adaptive Self-Supervised Learning for Real-World Image Denoisingcited by 33
  2. 2022Modeling sRGB Camera Noise with Normalizing Flowscited by 28
  3. 2022Learn from Unpaired Data for Image Restoration: A Variational Bayes Approachcited by 18
  4. 2023Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoisingcited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 4.

12 papers.

  1. 2024Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoisingcited by 6
  2. 2025Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Riverscited by 2
  3. 2025Blind-Spot Guided Diffusion for Self-supervised Real-World Denoisingcited by 2
  4. 2024FM2S: Towards Spatially-Correlated Noise Modeling in Zero-Shot Fluorescence Microscopy Image Denoisingcited by 1
  5. 2025Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoisingcited by 1
  6. 2026Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learningcited by 1
  7. 2025QWD-GAN: Quality-aware Wavelet-driven GAN for Unsupervised Medical Microscopy Images Denoisingcited by 0
  8. 2025Transforming Noise Distributions with Histogram Matching: Towards a Single Denoiser for Allcited by 0
  9. 2025Self-Supervised Compression and Artifact Correction for Streaming Underwater Imaging Sonarcited by 0
  10. 2026TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacousticscited by 0
  11. 2026Beyond Fixed Inference: Quantitative Flow Matching for Adaptive Image Denoisingcited by 0
  12. 2026Poisson2Gaussian: Noise Gaussianization to Enhance Image Denoisingcited by 0