  Video snapshot compressive imaging (SCI) captures multiple sequential video
frames by a single measurement using the idea of computational imaging. The
underlying principle is to modulate high-speed frames through different masks
and these modulated frames are summed to a single measurement captured by a
low-speed 2D sensor (dubbed optical encoder); following this, algorithms are
employed to reconstruct the desired high-speed frames (dubbed software decoder)
if needed. In this paper, we consider the reconstruction algorithm in video
SCI, i.e., recovering a series of video frames from a compressed measurement.
Specifically, we propose a Spatial-Temporal transFormer (STFormer) to exploit
the correlation in both spatial and temporal domains. STFormer network is
composed of a token generation block, a video reconstruction block, and these
two blocks are connected by a series of STFormer blocks. Each STFormer block
consists of a spatial self-attention branch, a temporal self-attention branch
and the outputs of these two branches are integrated by a fusion network.
Extensive results on both simulated and real data demonstrate the
state-of-the-art performance of STFormer. The code and models are publicly
available at https://github.com/ucaswangls/STFormer.git
