A Hybrid-3D Convolutional Network for Video Compressive Sensing

Video Compressive Sensing (VCS) works to recover the scene video from limited compressed measurements. VCS was intended to sense and recover the scene video in spatial-temporal sensing manner. It is difficult to be performed due to the complexity of design and optimization. The most current approaches measure the scene video only in the spatial or temporal domain. However, this would lose the spatial-temporal correlation in VCS. Focus on this issue, this paper proposes a VCS framework, which uses the learned spatial-temporal sensing manner and the hybrid-3D recovery network. In terms of technical study, we develop a hybrid-3D residual block consisting of Pseudo-3D and True-3D sub-blocks. This structure enables the network to intuitively represent the spatial-temporal feature, and significantly reduces the network parameters. In the detailed design, we explore the optimal hybrid-3D blocks. Experimentally, we validate the effectiveness of the learned spatial-temporal sensing manner. In addition, experimental results show that the proposed method achieves state-of-the-art performance on video sequences belonging to Vid 4 and SPMCS dataset, while the recovery speed is ultra-fast.