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Yu Sun, Zihui Wu, Brendt Wohlberg and Ulugbek S. Kamilov, "Scalable Plug-and-Play ADMM with Convergence Guarantees", arXiv, 2006.03224, 2020

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 large-scale datasets. 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.

BibTeX Entry

@techreport{sun-2020-scalable,
author = {Yu Sun and Zihui Wu and Brendt Wohlberg and Ulugbek S. Kamilov},
title = {Scalable Plug-and-Play ADMM with Convergence Guarantees},
year = {2020},
month = Jun,
urlpdf = {http://arxiv.org/pdf/2006.03224},
urlhtml = {http://arxiv.org/abs/2006.03224},
institution = {arXiv},
number = {2006.03224}
}