  Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture
sequential video frames and compress them into a single measurement. Various
reconstruction methods have been developed to recover the high-speed video
frames from the snapshot measurement. However, most existing reconstruction
methods are incapable of efficiently capturing long-range spatial and temporal
dependencies, which are critical for video processing. In this paper, we
propose a flexible and robust approach based on the graph neural network (GNN)
to efficiently model non-local interactions between pixels in space and time
regardless of the distance. Specifically, we develop a motion-aware dynamic GNN
for better video representation, i.e., represent each node as the aggregation
of relative neighbors under the guidance of frame-by-frame motions, which
consists of motion-aware dynamic sampling, cross-scale node sampling, global
knowledge integration, and graph aggregation. Extensive results on both
simulation and real data demonstrate both the effectiveness and efficiency of
the proposed approach, and the visualization illustrates the intrinsic dynamic
sampling operations of our proposed model for boosting the video SCI
reconstruction results. The code and model will be released.
