mlsanityData to 7 Oct 2026

Denoising Diffusion Restoration Models

arXiv 2201.11793 ↗First posted 27 Jan 2022eess.IV cs.CV cs.LGcited by 951

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Many interesting tasks in image restoration can be cast as linear inverse problems. A recent family of approaches for solving these problems uses stochastic algorithms that sample from the posterior distribution of natural images given the measurements. However, efficient solutions often require problem-specific supervised training to model the posterior, whereas unsupervised methods that are not problem-specific typically rely on inefficient iterative methods. This work addresses these issues by introducing Denoising Diffusion Restoration Models (DDRM), an efficient, unsupervised posterior sampling method. Motivated by variational inference, DDRM takes advantage of a pre-trained denoising diffusion generative model for solving any linear inverse problem. We demonstrate DDRM's versatility on several image datasets for super-resolution, deblurring, inpainting, and colorization under various amounts of measurement noise. DDRM outperforms the current leading unsupervised methods on the diverse ImageNet dataset in reconstruction quality, perceptual quality, and runtime, being 5x faster than the nearest competitor. DDRM also generalizes well for natural images out of the distribution of the observed ImageNet training set.

Key facts. Cited by 951 papers in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 43 of its 70 references that are in the map. First posted on arXiv 27 Jan 2022.

Builds on

43 of 70 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction9 Dec
  2. 2021Deblurring via Stochastic Refinement5 Dec

Other references · 41

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  7. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  8. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  9. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  10. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  11. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  12. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
29 more
  1. 2018Demystifying MMD GANscited by 1,268
  2. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  3. 2016Colorful Image Colorizationcited by 1,113
  4. 2017Deep Image Priorcited by 1,097
  5. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  6. 2017The Perception-Distortion Tradeoffcited by 582
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  8. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 467
  9. 2019DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Bettercited by 361
  10. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  11. 2017Compressed Sensing using Generative Modelscited by 337
  12. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 303
  13. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
  14. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  15. 2016Learning Representations for Automatic Colorizationcited by 251
  16. 2020Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulationcited by 225
  17. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
  18. 2021SNIPS: Solving Noisy Inverse Problems Stochasticallycited by 191
  19. 2019Image Processing Using Multi-Code GAN Priorcited by 185
  20. 2016Semantic Image Inpainting with Deep Generative Modelscited by 156
  21. 2020Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurringcited by 119
  22. 2021Bayesian imaging using Plug & Play priors: when Langevin meets Tweediecited by 96
  23. 2019DeepRED: Deep Image Prior Powered by REDcited by 84
  24. 2018An Online Plug-and-Play Algorithm for Regularized Image Reconstructioncited by 70
  25. 2021Intermediate Layer Optimization for Inverse Problems using Deep Generative Modelscited by 70
  26. 2017One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Modelscited by 67
  27. 2017Learned D-AMP: Principled Neural Network based Compressive Image Recoverycited by 65
  28. 2021Stochastic Image Denoising by Sampling from the Posterior Distributioncited by 45
  29. 2021Instance-Optimal Compressed Sensing via Posterior Samplingcited by 43

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 64; 2023: 211; 2024: 270; 2025: 221; 2026: 185.

951 papers.

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  1. 2023Consistency Modelscited by 1,778
  2. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
  3. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  4. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,172
  5. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  6. 2022Diffusion Models in Vision: A Surveycited by 1,008
  7. 2022eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoiserscited by 921
  8. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 574
  9. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  10. 2022MagicVideo: Efficient Video Generation With Latent Diffusion Modelscited by 463
  11. 2023Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusioncited by 448
  12. 2023Universal Guidance for Diffusion Modelscited by 426
38 more
  1. 2022PhysDiff: Physics-Guided Human Motion Diffusion Modelcited by 365
  2. 2023ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shiftingcited by 360
  3. 2023DeepCache: Accelerating Diffusion Models for Freecited by 346
  4. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  5. 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 311
  6. 2022MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolationcited by 307
  7. 2023FreeDoM: Training-Free Energy-Guided Conditional Diffusion Modelcited by 290
  8. 2024Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wildcited by 281
  9. 2022Cold Diffusion: Inverting Arbitrary Image Transforms Without Noisecited by 275
  10. 2023LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generationcited by 258
  11. 2023DiffBIR: Towards Blind Image Restoration with Generative Diffusion Priorcited by 255
  12. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 253
  13. 2023Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylizationcited by 251
  14. 2024One-Step Effective Diffusion Network for Real-World Image Super-Resolutioncited by 243
  15. 2022A Survey on Generative Diffusion Modelcited by 238
  16. 2023SinSR: Diffusion-Based Image Super-Resolution in a Single Stepcited by 235
  17. 2022MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generationcited by 225
  18. 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Modelscited by 218
  19. 2023A Variational Perspective on Solving Inverse Problems with Diffusion Modelscited by 217
  20. 2022Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Modelscited by 215
  21. 2023I2SB: Image-to-Image Schr"odinger Bridgecited by 214
  22. 2022Dual Diffusion Implicit Bridges for Image-to-Image Translationcited by 212
  23. 2024Diffusion Model-Based Image Editing: A Surveycited by 212
  24. 2023MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusioncited by 210
  25. 2023A Survey on Video Diffusion Modelscited by 203
  26. 2023Generative Diffusion Prior for Unified Image Restoration and Enhancementcited by 198
  27. 2023Image Restoration with Mean-Reverting Stochastic Differential Equationscited by 182
  28. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistencycited by 180
  29. 2024A Survey on Diffusion Models for Inverse Problemscited by 173
  30. 2022Generative Modelling With Inverse Heat Dissipationcited by 164
  31. 2023Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restorationcited by 164
  32. 2023DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statisticscited by 161
  33. 2024Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealingcited by 157
  34. 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Modelscited by 156
  35. 2024In-Context LoRA for Diffusion Transformerscited by 155
  36. 2023DDFM: Denoising Diffusion Model for Multi-Modality Image Fusioncited by 153
  37. 2023Manifold Preserving Guided Diffusioncited by 153
  38. 2023Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learningcited by 145

50 most cited of 951 All 951 (JSON)

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  1. 2025Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models
  2. 2024Diffusion Posterior Proximal Sampling for Image Restoration
  3. 2023SUD2: Supervision by Denoising Diffusion Models for Image Reconstruction
  4. 2024Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling
  5. 2023Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach
  6. 2024Decoupled Data Consistency with Diffusion Purification for Image Restoration
  7. 2025RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
  8. 2024Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration