2sER-VGSR-Net: A two-stage enhancement reconstruction based on video group sparse representation network for compressed video sensing

Compressed sensing (CS) requires fewer measurements than the Nyquist theory, making it excellent potential for video compression. However, the complex computation and long-running time limit the traditional compressed video sensing (CVS) methods in real-time application. In this paper, we proposed a fast CVS reconstruction based on deep network named 2sER-VGSR-Net. We first perform ISTA-Net+ as initial reconstruction. To exploit temporal and spatial correlation intrinsically, we construct a video inter-frame group by extracting blocks from the current and reference frames while establishing a sparse representation by network, called VGSR-Net. Different from traditional CVS methods, the group proposed in this paper contains fewer blocks thanks to the accurate alignment by STMC-Net. The inter-frame reconstruction comprises two stages, of which the first stage gets primary enhancement for motion compensation, and the latter performs as residual reconstruction to recover the details. Experiments show that the proposed 2sER-VGSR-Net outperforms the existing state-of-art CVS reconstruction algorithms.