摘要
分块压缩感知的提出很好地弥补了大尺寸图像占用资源多、重构耗时长等不足,但重构后的图像存在明显的块效应。针对现有图像纹理复杂度分析不够准确,导致自适应采样率分配后块效应降低不理想的问题,提出了一种基于灰度共生矩阵的图像自适应分块压缩感知方法。该方法通过共生矩阵分析图像的纹理特性,自适应分配采样率,在总采样率不变的前提下使纹理复杂度高的子块获得较高的采样率,纹理复杂度低的子块获得较低的采样率,并用SAMP(Sparsity Adaptive Matching Pursuit)算法实现重构。仿真结果显示,所提方法能够有效地解决块效应问题,尤其对于局部图像而言,重构图像的画质得到了明显改善。
The method of block compressed sensing really makes up for the defects of the consumed resource and time in reconstruction of large-size images.However,there is an obvious block effect in the reconstructed image.Aiming to solve the problem of inaccurate anlysis of texture complexity that hinders reduction of the block effect in adaptive sampling compressed sensing method,this paper proposed an adaptive block compressed sensing method based on co-occurrence matrices.The texture feature of image is analyzed by the co-occurrence matrix,and then the sampling rate is adaptively allocated according to the texture feature.Under the premise that the total sampling rate is not changed,the image with complex texture obtains higher sampling rate,and the image with simple texture obtains lower sampling rate.At last,SAMP（Sparsity Adaptive Matching Pursuit）is used to conduct reconstruction.The simulation results show that the proposed method can effectively eliminate the block effect,especially for the partial blocks,and the performance of the reconstructed block is obviously improved.
作者
杜秀丽
张薇
顾斌斌
陈波
邱少明
DU Xiu-li;ZHANG Wei;GU Bin-bin;CHEN Bo;QIU Shao-ming(Key Laboratory of Communications Network and Information Processing,Dalian University,Dalian 116622,China)
出处
《计算机科学》
CSCD
北大核心
2018年第8期277-282,共6页
Computer Science
基金
辽宁省教育厅高速眼图测试关键技术研究(L2014495)
辽宁"百千万人才工程"培养经费资助
关键词
分块压缩感知
灰度共生矩阵
熵
采样率
Block compressed sensing
Co-occurrence matrices
Entropy
Sampling rate
