| Paper ID | SPTM-12.2 |
| Paper Title |
GRAPH SIGNAL DENOISING USING NESTED-STRUCTURED DEEP ALGORITHM UNROLLING |
| Authors |
Masatoshi Nagahama, Koki Yamada, Yuichi Tanaka, Tokyo University of Agriculture and Technology, Japan; Stanley Chan, Purdue University, United States; Yonina C. Eldar, Weizmann Institute of Science, Israel |
| Session | SPTM-12: Sampling, Filtering and Denoising over Graphs |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SIPG] Signal and Information Processing over Graphs |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
