Graph Signal Denoising Using Nested-Structured Deep Algorithm Unrolling

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

ICASSP 2021accepted0 citations

Graph Signal Denoising Using Nested-Structured Deep Algorithm Unrolling

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

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.

BibTeX

@inproceedings{icassp2021_graphsignaldenoi,
  title = {Graph Signal Denoising Using Nested-Structured Deep Algorithm Unrolling},
  author = {Masatoshi Nagahama and Koki Yamada and Yuichi Tanaka and Stanley H. Chan and Yonina C. Eldar},
  booktitle = {ICASSP 2021},
  year = {2021}
}