A Flow-Guided Non-Local Alignment Network for Video Compressive Sensing Reconstruction

Video compressive sensing (VCS) presents a promising encoder paradigm for efficient video signals acquisition at resource-limited applications. In order to recover complete and accurate signals at the decoder, powerful reconstruction algorithms are desired to exploit rich temporal redundancies within video sequences. In this paper, we develop a flow-guided non-local alignment network (FNLAN), which can build accurate temporal dependencies among adjacent frames to help video recovery. The frames are aligned by non-local operations so that the subsequent fusion module can aggregate useful information from misaligned informative features. To avoid the prohibitive computation burden, the non-local operation is conducted within a local window guided by optical flows, i.e. the offsets derived from optical flows are enforced on the search window so that more correlated features are involved in non-local operations. Experimental results demonstrate the superiority of FNLAN.