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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Dingfei Jin & Guang Yang & Zhenghui Li & Haode Liu, 2019. "Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l 0 Norm and Randomized Coordinate Descent," Mathematics, MDPI, vol. 7(9), pages 1-13, September.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:9:p:834-:d:265687
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    References listed on IDEAS

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    1. Andrei Patrascu & Ion Necoara, 2015. "Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization," Journal of Global Optimization, Springer, vol. 61(1), pages 19-46, January.
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    Cited by:

    1. Hao Wang & Ruibin Feng & Chi-Sing Leung & Hau Ping Chan & Anthony G. Constantinides, 2022. "A Lagrange Programming Neural Network Approach with an ℓ 0 -Norm Sparsity Measurement for Sparse Recovery and Its Circuit Realization," Mathematics, MDPI, vol. 10(24), pages 1-22, December.

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