mlsanityData to 7 Oct 2026

An Online Plug-and-Play Algorithm for Regularized Image Reconstruction

arXiv 1809.04693 ↗First posted 12 Sep 2018cs.CV eess.IVcited by 70

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Abstract (arXiv)

Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse problems by using advanced denoisers within an iterative algorithm. Recent experimental evidence suggests that PnP algorithms achieve state-of-the-art performance in a range of imaging applications. In this paper, we introduce a new online PnP algorithm based on the iterative shrinkage/thresholding algorithm (ISTA). The proposed algorithm uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. We present a new theoretical convergence analysis, for both batch and online variants of PnP-ISTA, for denoisers that do not necessarily correspond to proximal operators. We also present simulations illustrating the applicability of the algorithm to image reconstruction in diffraction tomography. The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2018.

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Citing papers per year: 2021: 13; 2022: 16; 2023: 15; 2024: 11; 2025: 9; 2026: 6.

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  1. 2022Denoising Diffusion Restoration Modelscited by 951
  2. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
  3. 2021SNIPS: Solving Noisy Inverse Problems Stochasticallycited by 191
  4. 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 107
  5. 2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priorscited by 93
  6. 2024PnP-Flow: Plug-and-Play Image Restoration with Flow Matchingcited by 92
  7. 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
  8. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  9. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciencescited by 67
  10. 2023Provable Probabilistic Imaging using Score-Based Generative Priorscited by 51
  11. 2023Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidancecited by 43
  12. 2021End-to-end reconstruction meets data-driven regularization for inverse problemscited by 32
38 more
  1. 2023What's in a Prior? Learned Proximal Networks for Inverse Problemscited by 24
  2. 2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
  3. 2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
  4. 2021Fixed-Point and Objective Convergence of Plug-and-Play Algorithmscited by 19
  5. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  6. 2024Plug-and-Play image restoration with Stochastic deNOising REgularizationcited by 17
  7. 2023Provably Convergent Plug-and-Play Quasi-Newton Methodscited by 15
  8. 2022On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descentcited by 14
  9. 2023Designing Stable Neural Networks using Convex Analysis and ODEscited by 10
  10. 2023Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysiscited by 10
  11. 2022Deep Model-Based Architectures for Inverse Problems under Mismatched Priorscited by 8
  12. 2023Block Coordinate Plug-and-Play Methods for Blind Inverse Problemscited by 8
  13. 2023Self-Supervised Scalable Deep Compressed Sensingcited by 8
  14. 2022Memory-efficient model-based deep learning with convergence and robustness guaranteescited by 7
  15. 2025Deep Equilibrium models for Poisson Imaging Inverse problems via Mirror Descentcited by 7
  16. 2024Learning pseudo-contractive denoisers for inverse problemscited by 6
  17. 2024FiRe: Fixed-points of Restoration Priors for Solving Inverse Problemscited by 6
  18. 2022A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoiserscited by 5
  19. 2022Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-trainingcited by 5
  20. 2024Plug-and-Play Priors as a Score-Based Methodcited by 5
  21. 2021Stochastic Primal-Dual Deep Unrollingcited by 4
  22. 2022PAC-Bayesian Learning of Optimization Algorithmscited by 4
  23. 2025Learning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Inverse Problemscited by 4
  24. 2021Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decompositioncited by 3
  25. 2021Randomized Algorithms for Scientific Computing (RASC)cited by 3
  26. 2022Bregman Plug-and-Play Priorscited by 3
  27. 2022Monotonically Convergent Regularization by Denoisingcited by 3
  28. 2023Hierarchical Optimization-Derived Learningcited by 3
  29. 2023Single-Shot Plug-and-Play Methods for Inverse Problemscited by 3
  30. 2023An ℓ1-Plug-and-Play Approach for MPI Using a Zero Shot Denoiser with Evaluation on the 3D Open MPI Datasetcited by 3
  31. 2022Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradientscited by 2
  32. 2022REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restorationcited by 2
  33. 2023Convergence analysis of equilibrium methods for inverse problemscited by 2
  34. 2023Efficient Model-Based Deep Learning via Network Pruning and Fine-Tuningcited by 2
  35. 2024You KAN Do It in a Single Shot: Plug-and-Play Methods with Single-Instance Priorscited by 2
  36. 2025Plug-and-Play Posterior Sampling for Blind Inverse Problemscited by 2
  37. 2025An Iterative Reconstruction Method for Dental Cone-Beam Computed Tomography with a Truncated Field of Viewcited by 2
  38. 2025LVTINO: LAtent Video consisTency INverse sOlver for High Definition Video Restorationcited by 2

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