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Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg and Ulugbek S. Kamilov, "Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors", arXiv, 2010.01446, 2020

Abstract

Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors. Recent work has shown its state-of-the-art performance when combined with pre-trained deep denoisers. However, current RED algorithms are inadequate for parallel processing on multicore systems. We address this issue by proposing a new asynchronous RED (ASYNC-RED) algorithm that enables asynchronous parallel processing of data, making it significantly faster than its serial counterparts for large-scale inverse problems. The computational complexity of ASYNC-RED is further reduced by using a random subset of measurements at every iteration. We present complete theoretical analysis of the algorithm by establishing its convergence under explicit assumptions on the data-fidelity and the denoiser. We validate ASYNC-RED on image recovery using pre-trained deep denoisers as priors.

BibTeX Entry

@techreport{sun-2020-asyncred,
author = {Yu Sun and Jiaming Liu and Yiran Sun and Brendt Wohlberg and Ulugbek S. Kamilov},
title = {{Async-RED}: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors},
year = {2020},
month = Oct,
urlpdf = {http://arxiv.org/pdf/2010.01446},
urlhtml = {http://arxiv.org/abs/2010.01446},
institution = {arXiv},
number = {2010.01446}
}