Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics

Dingfei Jin¹  Haode Liu¹  Guang Yang²  Zhenghui Li³ 

[1] Central South University, CAD/CAM Institute, Changsha 410075, China;Zhengzhou Railway Vocational & Technical College, College of Railway Engineering, Zhengzhou 450000, China;Zhengzhou Railway Vocational & Technical College, Department of Foreign Affairs & Scientific Research, Zhengzhou 450000, China;

关键词: compressed sensing;    sparse recovery;    approximate (P₀) problem;    randomized coordinate descent;   

DOI  :  10.3390/math7090834

来源: DOAJ

【 摘 要 】

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 l₀ 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 (P⁰) 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.

【 授权许可】

Unknown