Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l 0 Norm and Randomized Coordinate Descent

Dingfei Jin, Guang Yang, Zhenghui Li and Haode Liu
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Dingfei Jin: Central South University, CAD/CAM Institute, Changsha 410075, China
Guang Yang: Zhengzhou Railway Vocational & Technical College, College of Railway Engineering, Zhengzhou 450000, China
Zhenghui Li: Zhengzhou Railway Vocational & Technical College, Department of Foreign Affairs & Scientific Research, Zhengzhou 450000, China
Haode Liu: Central South University, CAD/CAM Institute, Changsha 410075, China

Mathematics, 2019, vol. 7, issue 9, 1-13

Abstract: Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l 0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the ( P 0 ) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

Keywords: compressed sensing; sparse recovery; approximate ( P 0 ) problem; randomized coordinate descent (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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