中文摘要：

为了提高分布式视频压缩感知（Distributed Video Compressive Sensing，DVCS）的率失真性能，仅利用稀疏先验知识不能很好地保护视频帧的边缘与纹理细节，本文提出利用视频非局部相似性形成正则化项融入联合重构模型以有效去除边缘与纹理区域的模糊和块效应现象。仿真实验表明，本文所提出的联合重构算法可有效地改善主客观视频重构质量，能以一定计算复杂度为代价提高分布式视频压缩感知系统的率失真性能。

英文摘要：

To improve the rate-distortion performance of distributed video compressive sensing （DVCS）,The sparse pri-ors has only been exploited to not preserve the edges and textures of video frames well,the nonlocal similarity regularization term has been introduced to joint reconstruction model in order to effectively remove the blurs and blocking artifacts in the edge and texture regions.The simulation experiments show that the proposed joint reconstruction algorithm can effectively improve the objective and subjective quality of video,and enhance the rate-distortion performance of DVCS system at the cost of a certain computational complexity.