mlsanityData to 7 Oct 2026

Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning

arXiv 2409.06219 ↗First posted 10 Sep 2024cs.LG cs.CV eess.IVcited by 35

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Abstract (arXiv)

Denoising, the process of reducing random fluctuations in a signal to emphasize essential patterns, has been a fundamental problem of interest since the dawn of modern scientific inquiry. Recent denoising techniques, particularly in imaging, have achieved remarkable success, nearing theoretical limits by some measures. Yet, despite tens of thousands of research papers, the wide-ranging applications of denoising beyond noise removal have not been fully recognized. This is partly due to the vast and diverse literature, making a clear overview challenging. This paper aims to address this gap. We present a clarifying perspective on denoisers, their structure, and desired properties. We emphasize the increasing importance of denoising and showcase its evolution into an essential building block for complex tasks in imaging, inverse problems, and machine learning. Despite its long history, the community continues to uncover unexpected and groundbreaking uses for denoising, further solidifying its place as a cornerstone of scientific and engineering practice.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 59 of its 124 references that are in the map. First posted on arXiv 10 Sep 2024.

Builds on

59 of 124 references.

References · 59

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2022Flow Matching for Generative Modelingcited by 5,426
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  9. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  10. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  11. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  12. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
38 more
  1. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  2. 2022Denoising Diffusion Restoration Modelscited by 951
  3. 2017The Perception-Distortion Tradeoffcited by 582
  4. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  5. 2018Invertible Residual Networkscited by 364
  6. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  7. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 303
  8. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  9. 2021How to Train Your Energy-Based Modelscited by 271
  10. 2016Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applicationscited by 244
  11. 2023A Variational Perspective on Solving Inverse Problems with Diffusion Modelscited by 217
  12. 2021SNIPS: Solving Noisy Inverse Problems Stochasticallycited by 191
  13. 2019Full-Gradient Representation for Neural Network Visualizationcited by 189
  14. 2023Generalization in diffusion models arises from geometry-adaptive harmonic representationscited by 179
  15. 2023Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restorationcited by 164
  16. 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Modelscited by 156
  17. 2019Deep Learning on Image Denoising: An overviewcited by 128
  18. 2023Score-Based Diffusion Models as Principled Priors for Inverse Imagingcited by 126
  19. 2021Bayesian imaging using Plug & Play priors: when Langevin meets Tweediecited by 96
  20. 2022Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imagingcited by 94
  21. 2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priorscited by 93
  22. 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
  23. 2015Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolationcited by 78
  24. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  25. 2023Prompt-tuning latent diffusion models for inverse problemscited by 70
  26. 2020Regularization by Denoising via Fixed-Point Projection (RED-PRO)cited by 55
  27. 2024Tutorial on Diffusion Models for Imaging and Visioncited by 49
  28. 2023Closed-Form Diffusion Modelscited by 46
  29. 2021Stochastic Image Denoising by Sampling from the Posterior Distributioncited by 45
  30. 2021High Perceptual Quality Image Denoising with a Posterior Sampling CGANcited by 33
  31. 2018A characterization of proximity operatorscited by 32
  32. 2020Projected Distribution Loss for Image Enhancementcited by 29
  33. 2020Spectral convergence of diffusion maps: improved error bounds and an alternative normalisationcited by 25
  34. 2021Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithmscited by 24
  35. 2024Factorized Diffusion: Perceptual Illusions by Noise Decompositioncited by 23
  36. 2022PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularizationcited by 22
  37. 2015Proximal Algorithms in Statistics and Machine Learningcited by 18
  38. 2015Boosting of Image Denoising Algorithmscited by 13

50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 20; 2026: 11.

35 papers.

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  1. 2024A Survey on Diffusion Models for Inverse Problemscited by 173
  2. 2025Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorizationcited by 16
  3. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  4. 2025Kernel Density Steering: Inference-Time Scaling via Mode Seeking for Image Restorationcited by 5
  5. 2024Robust multi-coil MRI reconstruction via self-supervised denoisingcited by 4
  6. 2025Radio Map Estimation via Latent Domain Plug-and-Play Denoisingcited by 3
  7. 2025Transformer Meets Twicing: Harnessing Unattended Residual Informationcited by 3
  8. 2025Beyond Scores: Proximal Diffusion Modelscited by 3
  9. 2024Localized Schr"odinger Bridge Samplercited by 2
  10. 2025High Volume Rate 3D Ultrasound Reconstruction with Diffusion Modelscited by 2
  11. 2025Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulencecited by 2
  12. 2025From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlationscited by 2
23 more
  1. 2025Nonasymptotic Convergence Rates for Plug-and-Play Methods With MMSE Denoiserscited by 2
  2. 2025Latent Refinement via Flow Matching for Training-free Linear Inverse Problem Solvingcited by 2
  3. 2024On the Relation Between Linear Diffusion and Power Iterationcited by 1
  4. 2025Reconstruct Anything Model: a lightweight general model for computational imagingcited by 1
  5. 2025Solving Imaging Inverse Problems Using Plug-and-Play Denoisers: Regularization and Optimization Perspectivescited by 1
  6. 2025Energy-Tweedie: Score meets Score, Energy meets Energycited by 1
  7. 2026GSNR: Graph Smooth Null-Space Representation for Inverse Problemscited by 1
  8. 2026Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoisingcited by 1
  9. 2025Hypothesis Testing in Imaging Inverse Problemscited by 0
  10. 2025Physics-guided denoiser network for enhanced additive manufacturing data qualitycited by 0
  11. 2025Nuclear Diffusion Models for Low-Rank Background Suppression in Videoscited by 0
  12. 2025Net2Net: When Un-trained Meets Pre-trained Networks for Robust Real-World Denoisingcited by 0
  13. 2025DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learningcited by 0
  14. 2025Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problemscited by 0
  15. 2026A low-complexity method for efficient depth-guided image deblurringcited by 0
  16. 2026Learning Affine-Equivariant Proximal Operatorscited by 0
  17. 2026Measurement-Adapted Eigentask Representations for Photon-Limited Optical Readoutcited by 0
  18. 2026Bridging data-driven priors via the score function for posterior sampling -- Comparative review and experimental studycited by 0
  19. 2026Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusioncited by 0
  20. 2026Image Denoising via the Adaptive Rank-Cluster Filtercited by 0
  21. 2026Denoising as Projection: Constrained Optimization with Gradient-Guided Diffusioncited by 0
  22. 2026A Lagrangian View of Flow Matchingcited by 0
  23. 2026On the Diffusibility of High-Dimensional Latentscited by 0