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.

翻译：我们考虑的是视频快照压缩成像(SCI)的重建问题,它利用低速 2D 传感器(检测器)捕捉高速视频,在日常生活中使用高速2D 传感器(检测器)捕捉高速视频。SCI的根本原则是用不同的遮罩调制连续高速框架,然后将这些编码框架纳入传感器的快照中,因此传感器可以是低速的。一方面,视频SCI拥有低频、低功率和低成本框架的优势。另一方面,将SCI应用于我们日常生活中的大规模问题(HD 或UHD 视频)仍然具有挑战性,一个瓶颈在于重建算法中。退出算法要么太慢(模拟优化算法),要么对编码过程不灵活(深入学习基于端对端的网络)。在本文中,我们根据插放(PnP)框架为SCI开发快速和灵活的算法。除了P-ADMM方法外,我们进一步建议P-GAP(常规交替演算法)以更深的颜色算法来进行更新。我们第一次使用Sloeal-dealdealdeal imal deal degradual redustration exal redustration,然后又用Sdeal degradudududustration Sdeal daldeal exal 。我们用Slational exalal dislational disal dal