mlsanityData to 7 Oct 2026

Plug-and-Play Posterior Sampling for Blind Inverse Problems

arXiv 2505.22923 ↗First posted 28 May 2025eess.IV cs.CVcited by 2

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

Abstract (arXiv)

We introduce Blind Plug-and-Play Diffusion Models (Blind-PnPDM) as a novel framework for solving blind inverse problems where both the target image and the measurement operator are unknown. Unlike conventional methods that rely on explicit priors or separate parameter estimation, our approach performs posterior sampling by recasting the problem into an alternating Gaussian denoising scheme. We leverage two diffusion models as learned priors: one to capture the distribution of the target image and another to characterize the parameters of the measurement operator. This PnP integration of diffusion models ensures flexibility and ease of adaptation. Our experiments on blind image deblurring show that Blind-PnPDM outperforms state-of-the-art methods in terms of both quantitative metrics and visual fidelity. Our results highlight the effectiveness of treating blind inverse problems as a sequence of denoising subproblems while harnessing the expressive power of diffusion-based priors.

Key facts. Cited by 2 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 36 of its 42 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

36 of 42 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors29 May
  2. 2024Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction25 Mar
  3. 2023GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration30 Jan
  4. 2022Parallel Diffusion Models of Operator and Image for Blind Inverse Problems19 Nov
  5. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction9 Dec

Other references · 31

  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  3. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  4. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 467
  5. 2017Learning Deep CNN Denoiser Prior for Image Restorationcited by 407
  6. 2019DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Bettercited by 361
  7. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 303
  8. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
  9. 2016Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applicationscited by 244
  10. 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 197
  11. 2019Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernelscited by 112
  12. 2017Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problemscited by 107
19 more
  1. 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 107
  2. 2018Regularization by Denoising: Clarifications and New Interpretationscited by 102
  3. 2021Bayesian imaging using Plug & Play priors: when Langevin meets Tweediecited by 96
  4. 2017A Review of Convolutional Neural Networks for Inverse Problems in Imagingcited by 81
  5. 2018Denoising Prior Driven Deep Neural Network for Image Restorationcited by 80
  6. 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
  7. 2015Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolationcited by 78
  8. 2019Plug and play methods for magnetic resonance imaging (long version)cited by 75
  9. 2018An Online Plug-and-Play Algorithm for Regularized Image Reconstructioncited by 70
  10. 2017Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibriumcited by 58
  11. 2021Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fieldscited by 48
  12. 2018Split-and-augmented Gibbs sampler - Application to large-scale inference problemscited by 47
  13. 2018prDeep: Robust Phase Retrieval with a Flexible Deep Networkcited by 46
  14. 2023Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inferencecited by 41
  15. 2020Provable Convergence of Plug-and-Play Priors with MMSE denoiserscited by 39
  16. 2020Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problemscited by 38
  17. 2019Block Coordinate Regularization by Denoisingcited by 33
  18. 2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
  19. 2023Block Coordinate Plug-and-Play Methods for Blind Inverse Problemscited by 8

Built on by

Cited by year (2026 to 7 Oct)

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

2 papers.

  1. 2025PRISM: Probabilistic and Robust Inverse Solver with Measurement-Conditioned Diffusion Prior for Blind Inverse Problemscited by 2
  2. 2026When Unpaired Sets Support Shared-Corruption Calibration: Moment Geometry and Two-Sample Precisioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution
  2. 2024Blind Inversion using Latent Diffusion Priors
  3. 2025Plug-and-play Diffusion Models for Image Compressive Sensing with Data Consistency Projection
  4. 2024Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements
  5. 2025Diffusion models for inverse problems
  6. 2023A Variational Perspective on Solving Inverse Problems with Diffusion Models
  7. 2024Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method
  8. 2024Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion