提出了一种基于信号相关性的自适应时域视频压缩感知重建方法，在超时间分辨率视频成像过程中自适应地判断物体的运动量并有针对性地重建信号。该方法将所观察到的图像分成不同运动量大小的区域，然后利用由视频样本训练得到的对应的字典重建这些区域；在视频重建阶段，将编码曝光图像快速分块重建，再计算各帧图像块之间的相关系数，通过相关系数估计局部图像运动量，根据估计的运动量选择训练字典并重建图像。仿真实验结果表明，该方法能准确地获得视频图像的运动分布信息，在降低重建时间的同时提高了重建质量。

An adaptive temporal compressive sensing for video based on signal correlation is proposed, which can judge the motion of the object adaptively and reconstruct the signal targeted in the process of the super time resolution video imaging. The proposed method separates the observed image into regions of different amount-of-movement, and then reconstructs these regions with targeted dictionaries, which are trained from corresponding video samples. In the process of video reconstruction, block reconstruction of the coded exposure video is being done. This fast reconstructed video is used to compute the correlation coefficients between the neighbor frame image blocks. Local motions are then estimated by the correlation coefficients, and finally, the dictionaries can be adaptively selected according to the motion information to reconstruct the video. Simulation results show that the proposed method can obtain the motion distribution accurately, and the quality of the reconstructed video is increased while the reconstruction time is reduced.