Graph Signal Denoising Using Nested-Structured Deep Algorithm Unrolling

Masatoshi Nagahama, Koki Yamada, Yuichi Tanaka, Stanley H Chan, Yonina C Eldar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a deep algorithm unrolling (DAU) based on a variant of the alternating direction method of multiplier (ADMM) called Plug-and-Play ADMM (PnP-ADMM) for denoising of signals on graphs. DAU is a trainable deep architecture realized by unrolling iterations of an existing optimization algorithm which contains trainable parameters at each layer. We also propose a nested-structured DAU: Its submodules in the unrolled iterations are also designed by DAU. Several experiments for graph signal denoising are performed on synthetic signals on a community graph and U.S. temperature data to validate the proposed approach. Our proposed method outperforms alternative optimization- and deep learning-based approaches.
Original languageEnglish
Title of host publicationICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing
Pages5280-5284
Number of pages5
Volume2021-June
ISBN (Electronic)9781728176055
DOIs
StatePublished - 13 May 2021
EventIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Toronto, ON, Canada
Duration: 6 Jun 202111 Jun 2021

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Period6/06/2111/06/21

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