CaltechAUTHORS
  A Caltech Library Service

Scalable Plug-and-Play ADMM With Convergence Guarantees

Sun, Yu and Wu, Zihui and Xu, Xiaojian and Wohlberg, Brendt and Kamilov, Ulugbek S. (2021) Scalable Plug-and-Play ADMM With Convergence Guarantees. IEEE Transactions on Computational Imaging, 7 . pp. 849-863. ISSN 2333-9403. doi:10.1109/tci.2021.3094062. https://resolver.caltech.edu/CaltechAUTHORS:20211001-191837956

[img] PDF - Submitted Version
See Usage Policy.

3MB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20211001-191837956

Abstract

Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers. Recent work has reported the state-of-the-art performance of PnP algorithms using pre-trained deep neural nets as denoisers in a number of imaging applications. However, current PnP algorithms are impractical in large-scale settings due to their heavy computational and memory requirements. This work addresses this issue by proposing an incremental variant of the widely used PnP-ADMM algorithm, making it scalable to problems involving a large number measurements. We theoretically analyze the convergence of the algorithm under a set of explicit assumptions, extending recent theoretical results in the area. Additionally, we show the effectiveness of our algorithm with nonsmooth data-fidelity terms and deep neural net priors, its fast convergence compared to existing PnP algorithms, and its scalability in terms of speed and memory.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/tci.2021.3094062DOIArticle
https://arxiv.org/abs/2006.03224arXivDiscussion Paper
ORCID:
AuthorORCID
Sun, Yu0000-0001-7225-9677
Wu, Zihui0000-0002-7622-3548
Xu, Xiaojian0000-0002-5264-8963
Wohlberg, Brendt0000-0002-4767-1843
Kamilov, Ulugbek S.0000-0001-6770-3278
Additional Information:© 2021 IEEE. Manuscript received January 14, 2021; revised April 27, 2021 and June 27, 2021; accepted June 27, 2021. Date of publication July 2, 2021; date of current version August 14, 2021. This work supported in part by the National Science Foundation award CCF-1813910, by the the National Science Foundation CAREER award under Grant CCF-2043134, and by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project number 20200061DR. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Stanley H. Chan. (Yu Sun, Zihui Wu, and Xiaojian Xu contributed equally to this work.)
Funders:
Funding AgencyGrant Number
NSFCCF-1813910
NSFCCF-2043134
Los Alamos National Laboratory20200061DR
Subject Keywords:Regularized image reconstruction, plug-and-play priors, deep learning, regularization parameter
DOI:10.1109/tci.2021.3094062
Record Number:CaltechAUTHORS:20211001-191837956
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20211001-191837956
Official Citation:Y. Sun, Z. Wu, X. Xu, B. Wohlberg and U. S. Kamilov, "Scalable Plug-and-Play ADMM With Convergence Guarantees," in IEEE Transactions on Computational Imaging, vol. 7, pp. 849-863, 2021, doi: 10.1109/TCI.2021.3094062
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111155
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Oct 2021 20:26
Last Modified:04 Oct 2021 20:26

Repository Staff Only: item control page