@inproceedings{5ee53dda430d4454883068b89084b9c0,
title = "A reformative algorithm for compressed sensing sparse signal recovery based on smoothed L0 norm",
abstract = "The SL0 algorithm for compressed sensing (CS) is an convex programming iterative reconstruction algorithm, which construct a smooth function to approximate the L0 norm and transform the NP-hard problem of minimization of the L0 norm into a convex optimization problem of the smooth function. Aiming at its shortcomings, this paper proposes a faster and more efficient reconstruction algorithm (CG-SL0), which uses the inverse trigonometric fraction function to approximate the L0 norm, and uses the conjugate gradient method to achieve optimization. Experimental results show that, the CG-SL0 algorithm has significant advantages in reconstruction quality and performance under the same test conditions.",
keywords = "CG-SL0, Compressed sensing, Smoothed L0 norm, Sparse signal recovery",
author = "Yongtian Zhang and Xiaomei Chen and Chao Zeng and Kun Gao and Haitong Li",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; 9th Symposium on Novel Photoelectronic Detection Technology and Applications ; Conference date: 21-04-2023 Through 23-04-2023",
year = "2023",
doi = "10.1117/12.2664402",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Junhao Chu and Wenqing Liu and Hongxing Xu",
booktitle = "Ninth Symposium on Novel Photoelectronic Detection Technology and Applications",
address = "United States",
}