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LI Wenzong, HUA Gang. A compressive sensing measurement matrix for image signal[J]. Industry and Mine Automation，2022，48(1)：44-51. DOI: 10.13272/j.issn.1671-251x.2021070048
Citation: LI Wenzong, HUA Gang. A compressive sensing measurement matrix for image signal[J]. Industry and Mine Automation，2022，48(1)：44-51. DOI: 10.13272/j.issn.1671-251x.2021070048
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A compressive sensing measurement matrix for image signal

  • School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

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  • Received Date: July 17, 2021
  • Revised Date: December 09, 2021
  • Available Online: January 18, 2022
  • Published Date: January 19, 2022
    Graphical Abstract
    • Abstract
  • The amount of monitoring image information in unmanned working area of mine is large, and the hardware performance requirements are high in the image transmission and storage stage, which causes the problems of increased energy consumption and sudden decrease of the service life of sensor nodes. At present, when reconstructing mine monitoring image signal, the precision of compressive sensing measurement matrices such as Gause and Bernoulli is low. In order to solve the above problems, a new block Pascal compressive sensing measurement matrix (BPCSM) is designed. The BPCSM matrix uses the idea of non-uniform sampling and blocking in time domain, arranges multiple identical small-size Pascal matrices in a diagonal manner, and combines with the joint orthogonal matching tracking algorithm so as to realize the compression sampling and reconstruction of underground monitoring image signals. And the characteristics of orderly arrangement of row elements of Pascal matrices are used to strengthen the sampling of low frequency band of image signals so as to improve the reconstruction precision. The experimental results show that the reconstruction precision of BPCSM matrix for mine monitoring image signals is much higher than that of the commonly used measurement matrices such as Gause and Bernoulli. When the sampling rate is 0.3, the peak signal-to-noise ratio (PSNR) of the miner image reconstructed based on BPCSM matrix is about 26 dB, and the miner's facial contour is clear. When the sampling rate is 0.5, the PSNR of the miner image reconstructed based on BPCSM matrix has reached 30 dB, which can recover almost all the details of the miner image, indicating the better reconstruction performance of the BPCSM matrix. By selecting the appropriate Pascal matrix size, the reconstruction performance of the image signal can be further improved to meet the application requirements of the mine environment.
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    • References (20)
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