Low Storage Space of Random Measurement Matrix for Compressed Sensing with Semi-tensor Product

被引：0
|
作者：
Wang J.-M. [1 ]
Ye S.-P. [1 ]
Xu Z.-Y. [1 ]
Chen C.-X. [1 ]
Jiang Y.-J. [1 ]
机构：
[1] College of Information Science & Technology, Zhejiang Shuren University, Hangzhou, 310015, Zhejiang
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2018年 / 46卷 / 04期
关键词：
Compressed sensing; Random measurement matrix; Semi-tensor product (STP); Singular value decomposition (SVD); Storage space;
D O I：
10.3969/j.issn.0372-2112.2018.04.005
中图分类号：
学科分类号：
摘要：
Random measurement matrix needs large storage space, huge memory requirements for reconstruction, and high computational cost, which are not suitable for large-scale applications. To reduce the storage space of random measurement matrix for compressed sensing (CS), a new sampling approach for CS with semi-tensor product (STP-CS) is proposed. The STP-CS approach generates a random matrix, where the row and column numbers of the matrix are smaller than that for conventional CS. Then we optimize the matrix by the singular value decomposition (SVD) approach, after sampling with the matrix, we estimate the solutions of the sparse vector with the smooth ℓ0-norm minimization algorithm. Numerical experiments were conducted using gray-scale images, the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) of the reconstruction images were compared with the random matrices with different dimensions. Comparisons were also conducted with other random measurement matrix and other low storage techniques. Numerical experiment results show that the STP-CS can effectively reduce the storage space of the random measurement matrix to 1/256 of that for conventional CS, while maintaining the reconstruction performance. © 2018, Chinese Institute of Electronics. All right reserved.
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页码：797 / 804
页数：7
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