mlsanityData to 7 Oct 2026

Score-based Diffusion Models for Bayesian Image Reconstruction

arXiv 2305.16482 ↗First posted 25 May 2023eess.IVcited by 5

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

Abstract (arXiv)

This paper explores the use of score-based diffusion models for Bayesian image reconstruction. Diffusion models are an efficient tool for generative modeling. Diffusion models can also be used for solving image reconstruction problems. We present a simple and flexible algorithm for training a diffusion model and using it for maximum a posteriori reconstruction, minimum mean square error reconstruction, and posterior sampling. We present experiments on both a linear and a nonlinear reconstruction problem that highlight the strengths and limitations of the approach.

Key facts. Cited by 5 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 14 of its 24 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

14 of 24 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep
  2. 2022Denoising Diffusion Restoration Models27 Jan
  3. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction9 Dec

Other references · 11

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  5. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  6. 2019An Introduction to Variational Autoencoderscited by 997
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  8. 2021Score-based diffusion models for accelerated MRIcited by 308
  9. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
  10. 2019Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyondcited by 33
  11. 2017Convolutional Dictionary Learning: A Comparative Review and New Algorithmscited by 23

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024Estimating Epistemic and Aleatoric Uncertainty with a Single Modelcited by 52
  2. 2024Random Walks with Tweedie: A Unified View of Score-Based Diffusion Modelscited by 6
  3. 2024MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstructioncited by 4
  4. 2025Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imagingcited by 4
  5. 2026Stochastic Generative Plug-and-Play Priorscited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2024Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
  2. 2023Score-Based Diffusion Models as Principled Priors for Inverse Imaging
  3. 2024Bayesian Conditioned Diffusion Models for Inverse Problems
  4. 2023Variational Bayesian Imaging with an Efficient Surrogate Score-based Prior
  5. 2022How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
  6. 2024Bayesian computation with generative diffusion models by Multilevel Monte Carlo
  7. 2026Proximal-Based Generative Modeling for Bayesian Inverse Problems
  8. 2021Conditional Image Generation with Score-Based Diffusion Models