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

Journal Article · Thu Jan 17 00:00:00 EST 2019 · IEEE Transactions on Computational Imaging

DOI:https://doi.org/10.1109/TCI.2019.2893568· OSTI ID:1544682

Sun, Yu ^[1]; Wohlberg, Brendt Egon ^[2]; Kamilov, Ulugbek ^[1]

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1. Washington Univ., St. Louis, MO (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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. Here, we introduce a new online PnP algorithm based on the proximal gradient method (PGM). 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-PGM, 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. Finally, the results in this paper have the potential to expand the applicability of the PnP framework to very large datasets.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: Laboratory Directed Research and Development (LDRD); USDOE
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1544682
• Report Number(s): LA-UR--18-28977
• Journal Information: IEEE Transactions on Computational Imaging, Journal Name: IEEE Transactions on Computational Imaging Journal Issue: 3 Vol. 5; ISSN 2573-0436
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Regularized image reconstruction
alternating minimization
iterative thresholding
plug-andplay priors
regularization by denoising
stochastic optimization