  Snapshot compressive imaging (SCI) aims to capture the high-dimensional
(usually 3D) images using a 2D sensor (detector) in a single snapshot. Though
enjoying the advantages of low-bandwidth, low-power and low-cost, applying SCI
to large-scale problems (HD or UHD videos) in our daily life is still
challenging. The bottleneck lies in the reconstruction algorithms; they 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 widely used PnP-ADMM method, we further propose
the PnP-GAP (generalized alternating projection) algorithm with a lower
computational workload and prove the convergence of PnP-GAP under the SCI
hardware constraints. By employing deep denoising priors, we first time show
that PnP can recover a UHD color video ($3840\times 1644\times 48$ with PNSR
above 30dB) from a snapshot 2D measurement. Extensive results on both
simulation and real datasets verify the superiority of our proposed algorithm.
The code is available at https://github.com/liuyang12/PnP-SCI.

Image and Video Processing;Computer Vision and Pattern Recognition