  Video snapshot compressive imaging (SCI) aims to capture a sequence of video
frames with only a single shot of a 2D detector, whose backbones rest in
optical modulation patterns (also known as masks) and a computational
reconstruction algorithm. Advanced deep learning algorithms and mature hardware
are putting video SCI into practical applications. Yet, there are two clouds in
the sunshine of SCI: i) low dynamic range as a victim of high temporal
multiplexing, and ii) existing deep learning algorithms' degradation on real
system. To address these challenges, this paper presents a deep optics
framework to jointly optimize masks and a reconstruction network. Specifically,
we first propose a new type of structural mask to realize motion-aware and
full-dynamic-range measurement. Considering the motion awareness property in
measurement domain, we develop an efficient network for video SCI
reconstruction using Transformer to capture long-term temporal dependencies,
dubbed Res2former. Moreover, sensor response is introduced into the forward
model of video SCI to guarantee end-to-end model training close to real system.
Finally, we implement the learned structural masks on a digital micro-mirror
device. Experimental results on synthetic and real data validate the
effectiveness of the proposed framework. We believe this is a milestone for
real-world video SCI. The source code and data are available at
https://github.com/pwangcs/DeepOpticsSCI.
