为了提升视频压缩感知稀疏重构的准确性，得到更高质量的重构视频帧，基于视 频信号在不同表示域具有不同的稀疏特性，文中提出了一种基于多维度参考帧的双稀疏 重构算法( MＲF-DSＲ) ． 首先构建双稀疏重构模型，利用视频信号的组稀疏和拉普拉斯加 权稀疏特性来刻画重构视频帧的稀疏特性; 其次提出多维度参考帧的概念，引入基于时间 维度参考帧的半像素和缩放维度参考帧，通过为当前帧的图像块提供更多的可能匹配块 来获得稀疏度更高的匹配块组; 最后提出菱形形状快速搜索算法，通过粗搜索和精细搜索 过程确定时间维度参考帧最优相似块的位置，再在多维度参考帧的相同位置进行小范围 的快速搜索，从而实现较低复杂度的大范围搜索． 仿真实验结果表明，与现有最优视频压 缩感知重构算法相比， MＲF-DSＲ 算法在主观和客观标准上都具有较好的重构性能．

In order to improve the accuracy of sparse reconstruction based on compressed video sensing and achieve a higher quality of reconstructed video frames，considering videos' sparsity features in different domains，this paper proposes a dualsparsity reconstruction algorithm based on multi-dimension reference frames (MＲF-DSＲ) in compressed video sensing． Firstly，a dualsparsity reconstruction model is proposed that video frames group sparsity and laplacian sparsity are both utilized to restrict the reconstructed videos sparsity． Besides，the concept of multidimension reference frames is elaborated in this paper，where half-pixel dimension reference frames and scaling dimension reference frames based on time dimension reference frames are introduced to obtain match-block groups with higher sparsity． Lastly，a fast diamond searching algorithm is presented to implement largescale regional searching with low complexity，which，through the coarse and fine search process，determines the position of optimal time dimension reference frame similar block，then for quick search a small scale in the same position of dimensional reference frames． Experiment results manifest that the proposed MＲF-DSＲ outperforms the state-ofthe-art compressed video sensing reconstruction algorithm both on subjective and objective criteria．