A reformative algorithm for compressed sensing sparse signal recovery based on smoothed L0 norm

Yongtian Zhang; Xiaomei Chen; Chao Zeng; Kun Gao; Haitong Li

doi:10.1117/12.2664402

4 April 2023 A reformative algorithm for compressed sensing sparse signal recovery based on smoothed L0 norm

Yongtian Zhang, Xiaomei Chen, Chao Zeng, Kun Gao, Haitong Li

Proceedings Volume 12617, Ninth Symposium on Novel Photoelectronic Detection Technology and Applications; 126171I (2023) https://doi.org/10.1117/12.2664402
Event: 9th Symposium on Novel Photoelectronic Detection Technology and Applications (NDTA 2022), 2022, Hefei, China

Abstract

The SL0 algorithm for compressed sensing (CS) is a 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.

Citation Download Citation

Yongtian Zhang, Xiaomei Chen, Chao Zeng, Kun Gao, and Haitong Li "A reformative algorithm for compressed sensing sparse signal recovery based on smoothed L0 norm", Proc. SPIE 12617, Ninth Symposium on Novel Photoelectronic Detection Technology and Applications, 126171I (4 April 2023); https://doi.org/10.1117/12.2664402

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