  Video snapshot compressive imaging (SCI) captures a sequence of video frames
in a single shot using a 2D detector. The underlying principle is that during
one exposure time, different masks are imposed on the high-speed scene to form
a compressed measurement. With the knowledge of masks, optimization algorithms
or deep learning methods are employed to reconstruct the desired high-speed
video frames from this snapshot measurement. Unfortunately, though these
methods can achieve decent results, the long running time of optimization
algorithms or huge training memory occupation of deep networks still preclude
them in practical applications. In this paper, we develop a memory-efficient
network for large-scale video SCI based on multi-group reversible 3D
convolutional neural networks. In addition to the basic model for the grayscale
SCI system, we take one step further to combine demosaicing and SCI
reconstruction to directly recover color video from Bayer measurements.
Extensive results on both simulation and real data captured by SCI cameras
demonstrate that our proposed model outperforms previous state-of-the-art with
less memory and thus can be used in large-scale problems. The code is at
https://github.com/BoChenGroup/RevSCI-net.
