mlsanityData to 7 Oct 2026

CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image

arXiv 2203.13009 ↗First posted 24 Mar 2022cs.CVcited by 45

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Abstract (arXiv)

Recently, significant progress has been made on image denoising with strong supervision from large-scale datasets. However, obtaining well-aligned noisy-clean training image pairs for each specific scenario is complicated and costly in practice. Consequently, applying a conventional supervised denoising network on in-the-wild noisy inputs is not straightforward. Although several studies have challenged this problem without strong supervision, they rely on less practical assumptions and cannot be applied to practical situations directly. To address the aforementioned challenges, we propose a novel and powerful self-supervised denoising method called CVF-SID based on a Cyclic multi-Variate Function (CVF) module and a self-supervised image disentangling (SID) framework. The CVF module can output multiple decomposed variables of the input and take a combination of the outputs back as an input in a cyclic manner. Our CVF-SID can disentangle a clean image and noise maps from the input by leveraging various self-supervised loss terms. Unlike several methods that only consider the signal-independent noise models, we also deal with signal-dependent noise components for real-world applications. Furthermore, we do not rely on any prior assumptions about the underlying noise distribution, making CVF-SID more generalizable toward realistic noise. Extensive experiments on real-world datasets show that CVF-SID achieves state-of-the-art self-supervised image denoising performance and is comparable to other existing approaches. The code is publicly available from https://github.com/Reyhanehne/CVF-SID_PyTorch .

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 24 Mar 2022.

Builds on

14 of 35 references.

References · 14

  1. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  2. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 509
  3. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
  4. 2018Toward Convolutional Blind Denoising of Real Photographscited by 274
  5. 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 274
  6. 2017Benchmarking Denoising Algorithms with Real Photographscited by 209
  7. 2015Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restorationcited by 194
  8. 2019Real Image Denoising with Feature Attentioncited by 176
  9. 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 171
  10. 2019Noisier2Noise: Learning to Denoise from Unpaired Noisy Datacited by 125
  11. 2022C2N: Practical Generative Noise Modeling for Real-World Denoisingcited by 56
  12. 2017Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoisingcited by 41
2 more
  1. 2016Non-Local Color Image Denoising with Convolutional Neural Networkscited by 39
  2. 2019Deep K-SVD Denoisingcited by 26

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 19; 2024: 14; 2025: 5; 2026: 6.

45 papers.

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  1. 2023LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoisingcited by 37
  2. 2023Spatially Adaptive Self-Supervised Learning for Real-World Image Denoisingcited by 33
  3. 2023Random Sub-Samples Generation for Self-Supervised Real Image Denoisingcited by 31
  4. 2023MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Networkcited by 30
  5. 2023Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branchescited by 29
  6. 2024Transfer CLIP for Generalizable Image Denoisingcited by 29
  7. 2022Self-Supervised Image Restoration with Blurry and Noisy Pairscited by 17
  8. 2023Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarioscited by 17
  9. 2023Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Reviewcited by 17
  10. 2023Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoisingcited by 15
  11. 2023Self-Supervised Image Denoising for Real-World Images with Context-aware Transformercited by 13
  12. 2023Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Networkcited by 12
33 more
  1. 2024Masked and Shuffled Blind Spot Denoising for Real-World Imagescited by 12
  2. 2024Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoisingcited by 9
  3. 2023Recurrent Self-Supervised Video Denoising with Denser Receptive Fieldcited by 8
  4. 2023A Comparison of Image Denoising Methodscited by 7
  5. 2023Multi-view Self-supervised Disentanglement for General Image Denoisingcited by 7
  6. 2024Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restorationcited by 7
  7. 2024Asymmetric Mask Scheme for Self-Supervised Real Image Denoisingcited by 6
  8. 2024Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoisingcited by 6
  9. 2024Boosting Visual Recognition in Real-world Degradations via Unsupervised Feature Enhancement Module with Deep Channel Priorcited by 5
  10. 2024SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencodercited by 4
  11. 2023Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterizationcited by 3
  12. 2024Masked Pre-training Enables Universal Zero-shot Denoisercited by 3
  13. 2024Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformercited by 3
  14. 2023Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examplescited by 2
  15. 2025Blind-Spot Guided Diffusion for Self-supervised Real-World Denoisingcited by 2
  16. 2023One-Pot Multi-Frame Denoisingcited by 1
  17. 2023Blind2Sound: Self-Supervised Image Denoising without Residual Noisecited by 1
  18. 2023Medical Image Denosing via Explainable AI Feature Preserving Losscited by 1
  19. 2024FM2S: Towards Spatially-Correlated Noise Modeling in Zero-Shot Fluorescence Microscopy Image Denoisingcited by 1
  20. 2025Unsupervised Real-World Denoising: Sparsity is All You Needcited by 1
  21. 2025Next-Scale Prediction: A Self-Supervised Approach for Real-World Image Denoisingcited by 1
  22. 2026Why Low-Light Cameras Go Color Blind: Removing Color Bias in Raw Denoisingcited by 1
  23. 2023I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Imagescited by 0
  24. 2024Generalizing to Out-of-Sample Degradations via Model Reprogrammingcited by 0
  25. 2024Diffusion Priors for Variational Likelihood Estimation and Image Denoisingcited by 0
  26. 2024Adaptive Domain Learning for Cross-domain Image Denoisingcited by 0
  27. 2025QWD-GAN: Quality-aware Wavelet-driven GAN for Unsupervised Medical Microscopy Images Denoisingcited by 0
  28. 2025Net2Net: When Un-trained Meets Pre-trained Networks for Robust Real-World Denoisingcited by 0
  29. 2026TM-BSN: Triangular-Masked Blind-Spot Network for Real-World Self-Supervised Image Denoisingcited by 0
  30. 2026Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalizationcited by 0
  31. 2026Poisson2Gaussian: Noise Gaussianization to Enhance Image Denoisingcited by 0
  32. 2026Diffusion Based Unpaired Data Learning for Inverse Problemscited by 0
  33. 2026Noise2Noise Revisited: Training Pair Distributions Dominate Loss Choice in Self-Supervised Denoisingcited by 0