mlsanityData to 7 Oct 2026

Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation

arXiv 1512.07331 ↗First posted 23 Dec 2015cs.CV eess.IVcited by 78

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Abstract (arXiv)

Many material and biological samples in scientific imaging are characterized by non-local repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a 2D image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam. In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the non-local redundancy in images. We adapt a framework, termed plug-and-play (P&P) priors, to solve these imaging problems in a regularized inversion setting. The power of the P&P approach is that it allows a wide array of modern denoising algorithms to be used as a "prior model" for tomography and image interpolation. We also present sufficient mathematical conditions that ensure convergence of the P&P approach, and we use these insights to design a new non-local means denoising algorithm. Finally, we demonstrate that the algorithm produces higher quality reconstructions on both simulated and real electron microscope data, along with improved convergence properties compared to other methods.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Dec 2015.

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Citing papers per year: 2021: 16; 2022: 18; 2023: 17; 2024: 12; 2025: 8; 2026: 7.

78 papers.

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  1. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
  2. 2024A Survey on Diffusion Models for Inverse Problemscited by 173
  3. 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 107
  4. 2021Bayesian imaging using Plug & Play priors: when Langevin meets Tweediecited by 96
  5. 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
  6. 2022DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstructioncited by 73
  7. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  8. 2023Provable Probabilistic Imaging using Score-Based Generative Priorscited by 51
  9. 2021Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fieldscited by 48
  10. 2022Learned reconstruction methods with convergence guaranteescited by 46
  11. 2021Plug-and-Play Algorithms for Video Snapshot Compressive Imagingcited by 42
  12. 2021Self-supervised Neural Networks for Spectral Snapshot Compressive Imagingcited by 35
38 more
  1. 2024Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learningcited by 35
  2. 2021Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Priorcited by 24
  3. 2023Generative Plug and Play: Posterior Sampling for Inverse Problemscited by 24
  4. 2023What's in a Prior? Learned Proximal Networks for Inverse Problemscited by 24
  5. 2022Improving Lipschitz-Constrained Neural Networks by Learning Activation Functionscited by 23
  6. 2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
  7. 2022PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularizationcited by 22
  8. 2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
  9. 2021Fixed-Point and Objective Convergence of Plug-and-Play Algorithmscited by 19
  10. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  11. 2024Plug-and-Play image restoration with Stochastic deNOising REgularizationcited by 17
  12. 2022Approximation of Lipschitz Functions using Deep Spline Neural Networkscited by 15
  13. 2023Provably Convergent Plug-and-Play Quasi-Newton Methodscited by 15
  14. 2022On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descentcited by 14
  15. 2021Graph Signal Restoration Using Nested Deep Algorithm Unrollingcited by 13
  16. 2021Wasserstein Patch Prior for Image Superresolutioncited by 13
  17. 2023Designing Stable Neural Networks using Convex Analysis and ODEscited by 10
  18. 2023Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysiscited by 10
  19. 2021Deformation-Compensated Learning for Image Reconstruction without Ground Truthcited by 9
  20. 2022Deep Model-Based Architectures for Inverse Problems under Mismatched Priorscited by 8
  21. 2023Block Coordinate Plug-and-Play Methods for Blind Inverse Problemscited by 8
  22. 2024Patch-Based Diffusion Models Beat Whole-Image Models for Mismatched Distribution Inverse Problemscited by 8
  23. 2022PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descentcited by 7
  24. 2022CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoisercited by 6
  25. 2024Learning pseudo-contractive denoisers for inverse problemscited by 6
  26. 2022Deep Equilibrium Models for Video Snapshot Compressive Imagingcited by 5
  27. 2022CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mappingcited by 5
  28. 2023Convergent plug-and-play with proximal denoiser and unconstrained regularization parametercited by 5
  29. 2025MAP Estimation with Denoisers: Convergence Rates and Guaranteescited by 5
  30. 2022PAC-Bayesian Learning of Optimization Algorithmscited by 4
  31. 2022Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priorscited by 4
  32. 2023NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problemscited by 4
  33. 2025Learning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Inverse Problemscited by 4
  34. 2026Provable diffusion-based posterior sampling for linear inverse problems via DDIMcited by 4
  35. 2022Bregman Plug-and-Play Priorscited by 3
  36. 2022Supervision by Denoising for Medical Image Segmentationcited by 3
  37. 2022Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platformscited by 3
  38. 2023Convergence of Nonconvex PnP-ADMM with MMSE Denoiserscited by 3

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