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A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems

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arxiv 2006.11630 v2 pith:GKAAXVRB submitted 2020-06-20 math.OC cs.CV

Junqi Tang , Mike Davies This is my paper · ORCID
classification math.OCcs.CV
keywords stochasticmethodsimagingadmmalgorithmdescentgradientinverse
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In this work we propose an efficient stochastic plug-and-play (PnP) algorithm for imaging inverse problems. The PnP stochastic gradient descent methods have been recently proposed and shown improved performance in some imaging applications over standard deterministic PnP methods. However, current stochastic PnP methods need to frequently compute the image denoisers which can be computationally expensive. To overcome this limitation, we propose a new stochastic PnP-ADMM method which is based on introducing stochastic gradient descent inner-loops within an inexact ADMM framework. We provide the theoretical guarantee on the fixed-point convergence for our algorithm under standard assumptions. Our numerical results demonstrate the effectiveness of our approach compared with state-of-the-art PnP methods.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm

    eess.IV 2024-12 conditional novelty 6.0 of 10

    The authors prove that SNORE Prox, a stochastic proximal gradient descent using a denoiser, reaches a stationary point of its objective under nonconvex but weakly convex assumptions.

  2. Equivariant Denoisers for Image Restoration

    eess.IV 2024-12 conditional novelty 6.0 of 10

    ERED generalizes equivariant and stochastic plug-and-play denoisers into one framework, proves convergence and critical-point behavior, and finds only modest practical gains from equivariance.

  3. Domain Adaptation of Mismatched Proximal Denoiser for Plug-and-Play Image Reconstruction

    eess.IV 2026-07 conditional novelty 5.0 of 10

    For PnP-PGD, residual reconstruction error is bounded by average squared mismatch between the deployed denoiser and the target proximal map, motivating proximal-matching few-shot adaptation that outperforms MSE adapta...

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