基于LDPC测量矩阵的压缩感知图像重建  

Compressed Sensing Image Reconstruction Based on LDPC Measurement Matrix

作　　者：程彩凤 龙望晨[1] 林德树 CHENG Cai-feng;LONG Wang-chen;LIN De-shu(School of Computer and Information Engineering,Guangdong Songshan Polytechnic College,Shaoguan,512126,P.R.China;School of Electronics and Information,Yangtze University,Jingzhou 434023,P.R.China)

机构地区：[1]广东松山职业技术学院计算机与信息工程学院,广东韶关512126 [2]长江大学电子信息学院,湖北荆州434023

出　　处：《南方金属》2023年第4期1-4,共4页Southern Metals

基　　金：广东松山职业技术学院科研项目(2021KJYB003);广东省韶关市科技计划项目(210722104530538)。

摘　　要：测量矩阵是影响压缩感知信号重构效果的关键因素。为了研究重构性能好的测量矩阵,提出构造基于低密度奇偶校验码(LDPC)正则校验矩阵的测量矩阵。首先从理论上分析LDPC校验矩阵的性质,验证其有效性,然后采用渐进边增长算法构造矩阵,最后实验选取3个自然图像进行压缩采样重建,与常规的3种随机测量矩阵进行对比。实验结果表明,提出的测量矩阵重构性能良好,能提高压缩图像的分辨率、重建峰值信噪比,降低均方误差。Measurement matrix is the key factor that affects the reconstruction effect of compressed sensing signal.In order to study the measurement matrix with good reconstruction performance,a measurement matrix based on LDPC regular check matrix is proposed.Firstly,the properties of the LDPC check matrix are analyzed theoretically to verify its effectiveness,and then the matrix is constructed by using the progressive edge growth algorithm.Finally,three natural images are selected for compression sampling and reconstruction,and compared with the conventional three random measurement matrices.The experimental results show that the proposed measurement matrix reconstruction has good performance which can improve the resolution of compressed image and reconstruct the peak signal to noise ratio,and reduce the mean square error.

关 键 词：压缩感知 LDPC 测量矩阵 图像重建 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]