ZHANG Bo, LIU Yulin, JING Xiaojun, ZHUANG Jie, WANG Kai. Interweaving Permutation Meets Block Compressed Sensing[J]. Chinese Journal of Electronics, 2018, 27(5): 1056-1062. doi: 10.1049/cje.2017.04.001
Citation: ZHANG Bo, LIU Yulin, JING Xiaojun, ZHUANG Jie, WANG Kai. Interweaving Permutation Meets Block Compressed Sensing[J]. Chinese Journal of Electronics, 2018, 27(5): 1056-1062. doi: 10.1049/cje.2017.04.001

Interweaving Permutation Meets Block Compressed Sensing

doi: 10.1049/cje.2017.04.001
Funds:  This work is supported by the Program for New Century Excellent Talents in University of China (No.NCET-11-0873), the Key Project of Chongqing Natural Science Foundation (No.CSTC2011BA2016), and the Program for Fundamental and Advanced Research of Chongqing (No.cstc2013jcyjA40045).
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  • Corresponding author: JING Xiaojun (corresponding author) received the M.S. degree and Ph.D. degree from NUDT in 1995 and 1999 respectively, both in electronic science and engineering. From 2000 to 2002, he was a post-doctor at the Beijing University of Posts and Telecommunications (BUPT). Since 2002, he worked as a professor at School of Information and Communication Engineering, BUPT. His research interests include information security, image processing and information fusion. (Email:jxiaojun@bupt.edu.cn)
  • Received Date: 2016-05-17
  • Rev Recd Date: 2016-11-12
  • Publish Date: 2018-09-10
  • Traditional Block compressed sensing (BCS) schemes encode nature images via a fixed sampling rate without taking the sparsity level differences among the blocks into consideration. In order to improve the sampling efficiency, a permutation-based BCS scheme with separate reconstruction is considered in this paper. The error performance bound of BCS scheme is carefully analyzed, and it is revealed that the smaller the maximum block sparsity level of the 2D signal is, the better reconstruction performance the algorithm has. According to the theoretical analysis result, an interweaving-permutationbased BCS strategy is investigated. In the proposed approach, the maximum block sparsity level of the 2D signal can be reduced significantly by interweaving permutation. As a result, better reconstruction performance can be achieved. Simulation results show that the proposed approach improves the Peak signal-to-noise ratio (PSNR) of reconstructed-images significantly.
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