  We consider the reconstruction problem of video snapshot compressive imaging
(SCI), which captures high-speed videos using a low-speed 2D sensor (detector).
The underlying principle of SCI is to modulate sequential high-speed frames
with different masks and then these encoded frames are integrated into a
snapshot on the sensor and thus the sensor can be of low-speed. On one hand,
video SCI enjoys the advantages of low-bandwidth, low-power and low-cost. On
the other hand, applying SCI to large-scale problems (HD or UHD videos) in our
daily life is still challenging and one of the bottlenecks lies in the
reconstruction algorithm. Exiting algorithms are either too slow (iterative
optimization algorithms) or not flexible to the encoding process (deep learning
based end-to-end networks). In this paper, we develop fast and flexible
algorithms for SCI based on the plug-and-play (PnP) framework. In addition to
the PnP-ADMM method, we further propose the PnP-GAP (generalized alternating
projection) algorithm with a lower computational workload. We first employ the
image deep denoising priors to show that PnP can recover a UHD color video with
30 frames from a snapshot measurement. Since videos have strong temporal
correlation, by employing the video deep denoising priors, we achieve a
significant improvement in the results. Furthermore, we extend the proposed PnP
algorithms to the color SCI system using mosaic sensors, where each pixel only
captures the red, green or blue channels. A joint reconstruction and
demosaicing paradigm is developed for flexible and high quality reconstruction
of color video SCI systems. Extensive results on both simulation and real
datasets verify the superiority of our proposed algorithm.

Image and Video Processing;Computer Vision and Pattern Recognition