Ripple matrix permutation-based sparsity balanced block compressed sensing algorithm

doi:10.11772/j.issn.1001-9081.2018051039

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (12): 3541-3546.DOI: 10.11772/j.issn.1001-9081.2018051039

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Ripple matrix permutation-based sparsity balanced block compressed sensing algorithm

DU Xiuli, ZHANG Wei, CHEN Bo

Liaoning Provincial Key Laboratory of Communications Network and Information Processing(College of Information Engineering, Dalian University), Dalian Liaoning 116622, China

Received:2018-05-21 Revised:2018-08-02 Online:2018-12-10 Published:2018-12-15
Contact: 杜秀丽
Supported by:
This work is partially supported by the the Key Technology Research Project of High Speed Eye Diagram Test of Liaoning Provincial Department of Education (L2014495), the BaiQianWan Talents Program of Liaoning Province.

基于波浪式矩阵置换的稀疏度均衡分块压缩感知算法

杜秀丽, 张薇, 陈波

辽宁省通信网络与信息处理重点实验室(大连大学信息工程学院), 辽宁大连 116622

通讯作者: 杜秀丽
作者简介:杜秀丽(1977-),女,辽宁锦州人,教授,博士,CCF会员,主要研究方向:数字图像处理、压缩感知;张薇(1993-),女,河北保定人,硕士研究生,主要研究方向:数字图像处理、压缩感知;陈波(1972-),男,四川广安人,教授,博士,主要研究方向:智能检测。
基金资助:
辽宁省教育厅高速眼图测试关键技术研究基金资助项目（L2014495）；辽宁省"百千万人才工程"。

Abstract

Abstract: In matrix permutation-based Block Compressive Sensing (BCS), matrix permutation strategy is introduced, which makes the complex sub-blocks and sparse sub-blocks change to the middle level of sparsity and reduces the blocking artifacts when sampling with the single sampling rate. However there is still a problem of poor sparsity balance among blocks. In order to get better reconstruction effect, a Ripple Matrix Permutation-based sparsity balanced BCS (BCS-RMP) algorithm was proposed. Firstly, an image was pre-processed by matrix replacement before sampling, and the sparsity of each sub-block of the image was equalized by ripple permutation matrix. Then, a same measurement matrix was used to sample the sub-blocks and reconstruct them on the decoding side. Finally, the final reconstructed image was obtained by inverse transformation of reconstruction results by the ripple permutation inverse matrix. The simulation results show that, compared with the existing matrix replacement algorithms, the proposed ripple matrix permutation algorithm can effectively improve the quality of image reconstruction, and it can reflect the details more accurately when choosing appropriate sub-block size and sampling rate.

Key words: Block Compressed Sensing (BCS), matrix permutation, sparsity, measurement matrix, sampling rate

摘要： 基于矩阵置换的分块压缩感知（BCS）引入矩阵置换的策略，使复杂子块和稀疏子块向介于两者中间的稀疏度水平变化，用单一采样率采样时可以减少块效应，但仍存在块间稀疏度均衡效果较差的问题。为了得到更好的重构效果，提出基于波浪式矩阵置换的稀疏度均衡BCS（BCS-RMP）算法。首先，在采样前对图像进行矩阵置换的预处理，通过波浪式置换矩阵对图像各子块的稀疏度进行均衡；然后，采用相同的测量矩阵对子块进行采样，在解码侧进行重构；最后，通过波浪式置换逆矩阵对重构结果进行逆变换得到最终的重构图像。仿真结果表明，与现有矩阵置换算法相比，当选择合适的子块大小和采样率时，所提波浪式矩阵置换算法可有效提高图像的重构质量，且能更准确地体现细节信息。

关键词: 分块压缩感知, 矩阵置换, 稀疏度, 测量矩阵, 采样率

CLC Number:

TN911.73

DU Xiuli, ZHANG Wei, CHEN Bo. Ripple matrix permutation-based sparsity balanced block compressed sensing algorithm[J]. Journal of Computer Applications, 2018, 38(12): 3541-3546.

杜秀丽, 张薇, 陈波. 基于波浪式矩阵置换的稀疏度均衡分块压缩感知算法[J]. 计算机应用, 2018, 38(12): 3541-3546.

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Ripple matrix permutation-based sparsity balanced block compressed sensing algorithm

基于波浪式矩阵置换的稀疏度均衡分块压缩感知算法

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