A neighborhood-based multiple orthogonal least square method for sparse signal recovery

Orthogonal least squares (OLS) is a popular greedy algorithm because of its simplicity and low complex-ity. To improve the reconstruction accuracy performance of OLS, the multiple orthogonal least squares (MOLS) algorithm selects multiple L indices per iteration. Compared with OLS, MOLS improves the recon-struction accuracy and reduces the running time. However, the MOLS algorithm ignores the situation that the correct atom is in the neighborhood of the selected multiple L indices. We propose a neighborhood-based MOL S (NMOL S) algorithm in this study. The NMOLS algorithm incorporates a neighborhood-based strategy to find the more matching atoms in each iteration. NMOLS selects the support from the candi-date atoms by comparing their residual error. Simulation and image experimental results show that the proposed algorithm outperforms orthogonal matching pursuit (OMP), regularized OMP (ROMP), compres-sive sampling matching pursuit (CoSaMP), orthogonal least squares (OLS), and MOLS in terms of recon-struction accuracy.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This article presents the orthogonal least squares (OLS) algorithm and its limitations in improving reconstruction accuracy. A neighborhood-based multiple orthogonal least squares (NMOLS) algorithm is proposed to address this issue.