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Deep Video Compressive Sensing with Attention-Aware Bidirectional Propagation Network

Recent deep learning-based video compressive sensing (VCS) approaches have yielded impressive achievements on recovery performance, but the utilization of inter-frame correlations was still limited, thus leaving much room for further improvements. In this paper, we propose a novel VCS reconstruction network, named ABPN, to fully exploit the temporal correlations among neighboring frames. Different from previous works that only extract temporal information from key frames, the proposed network involves the whole sequence to propagate features bidirectionally, and each time step can directly extract long-term information from proceeding steps through temporally dense connections. Moreover, in each propagation branch, a flow-based alignment method is adopted to build accurate temporal correspondences between frames, and an attention-aware multi-frame fusion module (AMF) is proposed to encourage informative features and penalize ones with poor quality when aggregating aligned frames. Experimental results demonstrate the superiority of proposed network compared with other state-of-the-art approaches.