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

Sun, Yu; Wohlberg, Brendt Egon; Kamilov, Ulugbek

doi:10.1109/TCI.2019.2893568

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

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Abstract

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.

Authors:

Sun, Yu ^[1];

^[2]; Kamilov, Ulugbek ^[1]

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

Publication Date:: Thu Jan 17 00:00:00 EST 2019

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE Laboratory Directed Research and Development (LDRD) Program

OSTI Identifier:: 1544682

Report Number(s):: LA-UR-18-28977
Journal ID: ISSN 2573-0436

Grant/Contract Number:: 89233218CNA000001

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Computational Imaging

Additional Journal Information:: Journal Volume: 5; Journal Issue: 3; Journal ID: ISSN 2573-0436

Publisher:: IEEE

Country of Publication:: United States

Language:: English

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

Citation Formats


                    Sun, Yu, Wohlberg, Brendt Egon, and Kamilov, Ulugbek. An Online Plug-and-Play Algorithm for Regularized Image Reconstruction.  United States: N. p., 2019. 
Web.  doi:10.1109/TCI.2019.2893568.

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                    Sun, Yu, Wohlberg, Brendt Egon, & Kamilov, Ulugbek. An Online Plug-and-Play Algorithm for Regularized Image Reconstruction.  United States.  https://doi.org/10.1109/TCI.2019.2893568

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                    Sun, Yu, Wohlberg, Brendt Egon, and Kamilov, Ulugbek. Thu .  
"An Online Plug-and-Play Algorithm for Regularized Image Reconstruction".  United States.  https://doi.org/10.1109/TCI.2019.2893568.  https://www.osti.gov/servlets/purl/1544682.

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@article{osti_1544682,

  title        = {An Online Plug-and-Play Algorithm for Regularized Image Reconstruction},

  author       = {Sun, Yu and Wohlberg, Brendt Egon and Kamilov, Ulugbek},

  abstractNote = {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.},

  doi          = {10.1109/TCI.2019.2893568},

  journal      = {IEEE Transactions on Computational Imaging},

  number       = 3,

  volume       = 5,

  place        = {United States},

  year         = {Thu Jan 17 00:00:00 EST 2019},

  month        = {Thu Jan 17 00:00:00 EST 2019}

}

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