Graph Signal Restoration Using Nested Deep Algorithm Unrolling

• Published in: IEEE transactions on signal processing Vol. 70; pp. 3296 - 3311
• Main Authors: Nagahama, Masatoshi, Yamada, Koki, Tanaka, Yuichi, Chan, Stanley H., Eldar, Yonina C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computational geometry; Convexity; deep algorithm unrolling; Graph neural networks; Graph signal processing; Image restoration; Interpolation; Neural networks; Noise reduction; Optimization; Parameters; Plug-and-Play ADMM; Regularization; Restoration; Signal processing; Signal processing algorithms; Signal restoration; Symmetric matrices; Task analysis
• ISSN: 1053-587X; 1941-0476; 1941-0476
• DOI: 10.1109/TSP.2022.3180546

Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Often, graph signals are corrupted in the sensing process, thus requiring restoration. In this paper, we propose two graph signal restoration methods based on deep algorithm unrolling (DAU). First, we present a graph signal denoiser by unrolling iterations of the alternating direction method of multiplier (ADMM). We then suggest a general restoration method for linear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In the second approach, the unrolled ADMM-based denoiser is incorporated as a submodule, leading to a nested DAU structure. The parameters in the proposed denoising/restoration methods are trainable in an end-to-end manner. Our approach is interpretable and keeps the number of parameters small since we only tune graph-independent regularization parameters. We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually. 2) large number of parameters of graph neural networks that result in difficulty of training. Several experiments for graph signal denoising and interpolation are performed on synthetic and real-world data. The proposed methods show performance improvements over several existing techniques in terms of root mean squared error in both tasks.