电子学报2018，Vol.46Issue(4)：797-804,8.DOI:10.3969/j.issn.0372-2112.2018.04.005
半张量积低存储压缩感知方法研究
Low Storage Space of Random Measurement Matrix for Compressed Sensing with Semi-tensor Product
摘要
Abstract
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 e0-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.关键词
压缩感知/随机观测矩阵/半张量积/存储空间/奇异值分解Key words
compressed sensing/random measurement matrix/semi-tensor product (STP)/storage space/singular value decomposition (SVD)分类
信息技术与安全科学引用本文复制引用
王金铭,叶时平,徐振宇,陈超祥,蒋燕君..半张量积低存储压缩感知方法研究[J].电子学报,2018,46(4):797-804,8.基金项目
浙江省自然科学基金(No.LY14E070001) （No.LY14E070001）
浙江省公益技术应用研究计划(No.2015C33074,No.2015C33083) （No.2015C33074,No.2015C33083）
浙江省科技计划(No.2014C33058) （No.2014C33058）
