Data to 14 Oct 2026

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging

arXiv 2203.17061 ↗First posted 31 Mar 2022eess.IVcited by 96

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Abstract (arXiv)

Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data-fidelity term to promote data consistency and imposing a learned regularizer in the form of an image denoiser. Recent highly-successful applications of PnP algorithms include bio-microscopy, computerized tomography, magnetic resonance imaging, and joint ptycho-tomography. This article presents a unified and principled review of PnP by tracing its roots, describing its major variations, summarizing main results, and discussing applications in computational imaging. We also point the way towards further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms.

Key facts. Cited by 96 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Mar 2022.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 24; 2024: 25; 2025: 26; 2026: 19.

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2024A Survey on Diffusion Models for Inverse Problemscited by 180
2024Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealingcited by 163
2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priorscited by 97
2023Prompt-tuning latent diffusion models for inverse problemscited by 70
2023Provable Probabilistic Imaging using Score-Based Generative Priorscited by 51
2023Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidancecited by 43
2023Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolutioncited by 36
2024Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learningcited by 35
2023Equivariant plug-and-play image reconstructioncited by 26
2023What's in a Prior? Learned Proximal Networks for Inverse Problemscited by 25
2024DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstructioncited by 17
2023Provably Convergent Plug-and-Play Quasi-Newton Methodscited by 15

38 more

2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstructioncited by 15
2024Repulsive Latent Score Distillation for Solving Inverse Problemscited by 15
2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstructioncited by 14
2023Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurringcited by 14
2024Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisationcited by 14
2023Convergent regularization in inverse problems and linear plug-and-play denoiserscited by 13
2024Invertible Diffusion Models for Compressed Sensingcited by 12
2023Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysiscited by 11
2024Deep Learning for Accelerated and Robust MRI Reconstruction: a Reviewcited by 9
2023Self-Supervised Scalable Deep Compressed Sensingcited by 8
2025Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imagingcited by 8
2022Memory-efficient model-based deep learning with convergence and robustness guaranteescited by 7
2023Unfolded proximal neural networks for robust image Gaussian denoisingcited by 7
2025Deep Equilibrium models for Poisson Imaging Inverse problems via Mirror Descentcited by 7
2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
2023Model-corrected learned primal-dual models for fast limited-view photoacoustic tomographycited by 6
2022Compressive Ptychography using Deep Image and Generative Priorscited by 5
2025Good Things Come in Pairs: Paired Autoencoders for Inverse Problemscited by 5
2025MAP Estimation with Denoisers: Convergence Rates and Guaranteescited by 5
2026Self-Supervised Learning from Noisy and Incomplete Datacited by 5
2023NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problemscited by 4
2023Provably Convergent Data-Driven Convex-Nonconvex Regularizationcited by 4
2025Learning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Inverse Problemscited by 4
2025NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problemscited by 4
2023Learning Task-Specific Strategies for Accelerated MRIcited by 3
2024Schur's Positive-Definite Network: Deep Learning in the SPD cone with structurecited by 3
2024A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modelingcited by 3
2025Radio Map Estimation via Latent Domain Plug-and-Play Denoisingcited by 3
2025INDIGO+: A Unified INN-Guided Probabilistic Diffusion Algorithm for Blind and Non-Blind Image Restorationcited by 3
2025PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Priorcited by 3
2023RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imagingcited by 2
2023Convergence analysis of equilibrium methods for inverse problemscited by 2
2024MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstructioncited by 2
2024Learning truly monotone operators with applications to nonlinear inverse problemscited by 2
2024Parseval Convolution Operators and Neural Networkscited by 2
2024LoFi: Neural Local Fields for Scalable Image Reconstructioncited by 2
2025RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imagingcited by 2
2025Restarted contractive operators to learn at equilibriumcited by 2

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