Scalable Plug-and-Play ADMM with Convergence Guarantees

Sun, Yu; Wu, Zihui; Xu, Xiaojian; Wohlberg, Brendt Egon; Kamilov, Ulugbek

doi:10.1109/TCI.2021.3094062

Scalable Plug-and-Play ADMM with Convergence Guarantees

Journal Article · Fri Jul 02 00:00:00 EDT 2021 · IEEE Transactions on Computational Imaging

DOI:https://doi.org/10.1109/TCI.2021.3094062· OSTI ID:1825405

Sun, Yu ^[1]; Wu, Zihui ^[2]; Xu, Xiaojian ^[1]; ^[3]; Kamilov, Ulugbek ^[1]

Washington Univ., St. Louis, MO (United States)
California Institute of Technology (CalTech), Pasadena, CA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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. Here, 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.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: LDRD

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1825405

Report Number(s):: LA-UR--20-24233

Journal Information:: IEEE Transactions on Computational Imaging, Journal Name: IEEE Transactions on Computational Imaging Vol. 7; ISSN 2573-0436

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Scalable Plug-and-Play ADMM with Convergence Guarantees

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