An Efficient Algorithm with Fast Convergence Rate for Sparse Graph Signal Reconstruction
Yuting Cao, Xueqin Jiang, Jian Wang, Shubo Zhou, Xinxin Hou · Research Square · 2023
Abstract 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 (CS) by considering a threshold for sparsity-promoting reconstruction of the under- lying graph signals. The proposed ITCSR algorithm enhances the performance of sparse graph signal reconstruction by introducing a threshold function to determine an suitable threshold. Furthermore, we demonstrate that the suitable parameters for the threshold can be automatically determined by leveraging the sparrow search algorithm (SSA). Morever, 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.