An efficient algorithm with fast convergence rate for sparse graph signal reconstruction

• Published in: EURASIP journal on advances in signal processing Vol. 2024; no. 1; pp. 38 - 23
• Main Authors: Cao, Yuting, Jiang, Xue-Qin, Wang, Jian, Zhou, Shubo, Hou, Xinxin
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2024; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Advanced Signal Processing Algorithms and Techniques for Machinery Diagnosis; Algorithms; Compressed sensing (CS); Convergence; Datasets; Engineering; Graph Fourier domain; Iterative methods; Iterative threshold compressed sensing reconstruction (ITCSR); Quantum Information Technology; Search algorithms; Signal processing; Signal reconstruction; Signal,Image and Speech Processing; Sparse graph signals; Sparsity; Spintronics; Three dimensional models; Graph Fourier domain; Iterative threshold compressed sensing reconstruction (ITCSR); Compressed sensing (CS); Sparse graph signals
• ISSN: 1687-6180; 1687-6172; 1687-6180
• DOI: 10.1186/s13634-024-01133-3

In this paper, we consider the graph signals are sparse in the graph Fourier domain and propose an iterative threshold compressed sensing reconstruction (ITCSR) algorithm to reconstruct sparse graph signals in the graph Fourier domain. The proposed ITCSR algorithm derives from the well-known compressed sensing by considering a threshold for sparsity-promoting reconstruction of the underlying graph signals. The proposed ITCSR algorithm enhances the performance of sparse graph signal reconstruction by introducing a threshold function to determine a suitable threshold. Furthermore, we demonstrate that the suitable parameters for the threshold can be automatically determined by leveraging the sparrow search algorithm. Moreover, we analytically prove the convergence property of the proposed ITCSR algorithm. In the experimental, numerical tests with synthetic as well as 3D point cloud data demonstrate the merits of the proposed ITCSR algorithm relative to the baseline algorithms.