基于纹理自适应全变分滤波的图像分块压缩感知优化算法  

Enhanced Block Compressed Sensing of Images Based on Total Variation Using Texture Information

作　　者：王玥[1] 周城[1] 熊承义[1] 舒振宇[1] 

机构地区：[1]中南民族大学电子信息工程学院智能无线通信湖北省重点实验室,武汉430074

出　　处：《计算机科学》2016年第2期307-310,315,共5页Computer Science

基　　金：国家自然科学基金(61201268;61471400);湖北省自然科学基金(2014CFB913);中央高校科研基本业务费专项(CZW15042)资助

摘　　要：图像分块压缩感知重构模型通过分块方式解决了压缩感知中观测矩阵过大带来的计算复杂度较高和存储空间较大的问题,但分块重构时会产生块效应,其需要通过去块效应滤波加以消除。现有的滤波方法并未考虑图像纹理细节恢复问题,造成了重构质量的降低。为解决该问题,首先提出了一种基于灰度熵的纹理自适应采样方法。随后分析了分块压缩感知中块效应的产生和经自适应采样后块效应得到缓解的原因,并将全变分滤波引入到图像分块压缩感知平滑投影迭代重构过程之中,提出了一种基于图像分块纹理信息的双树离散小波硬阈值滤波和全变分滤波的自适应加权滤波模型,用其取代原平滑投影迭代算法的滤波过程,在自适应采样缓解块效应的基础上,更有效地保存图像的细节信息。仿真实验表明,与多种已有方案相比,该方案可显著提升重建图像的主客观质量,同时可有效保留图像的纹理细节。Block compressed sensing of images solves the problems of high computational complexity and large storage space required by blocking an image and downsizing measurement matrix.But such a practice will result in blocking artifacts,which needs to be filtered.Existing algorithms do not consider how to recover textural features of images,which will result in quality degradation of image reconstruction.In order to solve this problem,this paper proposed an algorithm which uses an adaptive sampling model based on gray entropy at first,and then analyzed the reason why blocking artifacts generate and are reduced by adaptive sampling.At last,in the proposed algorithm TV filter is joined with SPL process,and a DDWT/TV filter model based on texture information is built to replace the former filtering process in reconstruction.The model can preserve more details of images after decreasing block artifacts by using adaptive sampling.Experimental results show that the proposed algorithm can remarkably improve the subjective and objective quality of the reconstructed image and can effectively hold more texture information of images compared to some existing methods.

关 键 词：分块压缩感知 自适应采样率 全变分滤波 去块效应滤波 

分 类 号：TN919.8[电子电信—通信与信息系统]