# Diffusion Model for Generative Image Denoising arXiv: https://arxiv.org/abs/2302.02398 · HTML: https://mlsanity.com/paper/2302.02398/ ## Key facts - Cited by 28 papers in the map (data through 14 Oct 2026). - Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. - Builds on 17 of its 33 references that are in the map. - First posted on arXiv 5 Feb 2023. ## Placed in - Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/) ## Builds on (most cited first, 17 of 17) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015) - [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021) - [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016) - [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020) - [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017) - [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020) - [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018) - [CycleISP: Real Image Restoration via Improved Data Synthesis](https://mlsanity.com/paper/2003.07761/) (2020) - [Real Image Denoising with Feature Attention](https://mlsanity.com/paper/1904.07396/) (2019) - [Variational Denoising Network: Toward Blind Noise Modeling and Removal](https://mlsanity.com/paper/1908.11314/) (2019) - [Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization](https://mlsanity.com/paper/2002.11244/) (2020) - [NBNet: Noise Basis Learning for Image Denoising with Subspace Projection](https://mlsanity.com/paper/2012.15028/) (2020) - [Invertible Denoising Network: A Light Solution for Real Noise Removal](https://mlsanity.com/paper/2104.10546/) (2021) - [Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer](https://mlsanity.com/paper/2202.03674/) (2022) ## Built on by (most cited first, 28 of 28) - [Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2308.09388/) (2023) - [IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination](https://mlsanity.com/paper/2404.11593/) (2024) - [Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling](https://mlsanity.com/paper/2307.03992/) (2023) - [DiffDP: Radiotherapy Dose Prediction via a Diffusion Model](https://mlsanity.com/paper/2307.09794/) (2023) - [MD-Dose: A diffusion model based on the Mamba for radiation dose prediction](https://mlsanity.com/paper/2403.08479/) (2024) - [A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models](https://mlsanity.com/paper/2403.12052/) (2024) - [On the Asymptotic Mean Square Error Optimality of Diffusion Models](https://mlsanity.com/paper/2403.02957/) (2024) - [Diff-Shadow: Global-guided Diffusion Model for Shadow Removal](https://mlsanity.com/paper/2407.16214/) (2024) - [SVNR: Spatially-variant Noise Removal with Denoising Diffusion](https://mlsanity.com/paper/2306.16052/) (2023) - [Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding](https://mlsanity.com/paper/2311.02991/) (2023) - [Listening to the Noise: Blind Denoising with Gibbs Diffusion](https://mlsanity.com/paper/2402.19455/) (2024) - [Inverse Flow and Consistency Models](https://mlsanity.com/paper/2502.11333/) (2025) - [RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model](https://mlsanity.com/paper/2408.16634/) (2024) - [ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Models](https://mlsanity.com/paper/2406.12044/) (2024) - [SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction Based on Multi-Scale Fusion of Anatomical Structures, Guided by SwinTransformer and Projector](https://mlsanity.com/paper/2312.06187/) (2023) - [Visual Privacy Auditing with Diffusion Models](https://mlsanity.com/paper/2403.07588/) (2024) - [ODE-DPS: ODE-based Diffusion Posterior Sampling for Inverse Problems in Partial Differential Equation](https://mlsanity.com/paper/2404.13496/) (2024) - [Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Bootstrapping](https://mlsanity.com/paper/2501.18962/) (2025) - [DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models](https://mlsanity.com/paper/2312.04853/) (2023) - [Controlling the Latent Diffusion Model for Generative Image Shadow Removal via Residual Generation](https://mlsanity.com/paper/2412.02322/) (2024) - [A Simple Combination of Diffusion Models for Better Quality Trade-Offs in Image Denoising](https://mlsanity.com/paper/2503.14654/) (2025) - [Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review](https://mlsanity.com/paper/2505.07866/) (2025) - [Investigating Self-Supervised Image Denoising with Denaturation](https://mlsanity.com/paper/2405.01124/) (2024) - [Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation](https://mlsanity.com/paper/2509.11485/) (2025) - [PedestrianDiffusion: Multimodal Generative Denoising and Dense State Estimation for Inertial Navigation](https://mlsanity.com/paper/2607.03349/) (2026) - [γ-Bridge: A Look-Parametric Diffusion Bridge](https://mlsanity.com/paper/2607.22719/) (2026) - [On the Diffusibility of High-Dimensional Latents](https://mlsanity.com/paper/2609.28473/) (2026) - [One Photon, Many Worlds: Posteriors and Predictions with Single-Photon Cameras](https://mlsanity.com/paper/2610.02675/) (2026) ## Abstract (arXiv) In supervised learning for image denoising, usually the paired clean images and noisy images are collected or synthesised to train a denoising model. L2 norm loss or other distance functions are used as the objective function for training. It often leads to an over-smooth result with less image details. In this paper, we regard the denoising task as a problem of estimating the posterior distribution of clean images conditioned on noisy images. We apply the idea of diffusion model to realize generative image denoising. According to the noise model in denoising tasks, we redefine the diffusion process such that it is different from the original one. Hence, the sampling of the posterior distribution is a reverse process of dozens of steps from the noisy image. We consider three types of noise model, Gaussian, Gamma and Poisson noise. With the guarantee of theory, we derive a unified strategy for model training. Our method is verified through experiments on three types of noise models and achieves excellent performance.