  Video Snapshot compressive imaging (SCI) is a promising technique to capture
high-speed videos, which transforms the imaging speed from the detector to mask
modulating and only needs a single measurement to capture multiple frames. The
algorithm to reconstruct high-speed frames from the measurement plays a vital
role in SCI. In this paper, we consider the promising reconstruction algorithm
framework, namely plug-and-play (PnP), which is flexible to the encoding
process comparing with other deep learning networks. One drawback of existing
PnP algorithms is that they use a pre-trained denoising network as a plugged
prior while the training data of the network might be different from the task
in real applications. Towards this end, in this work, we propose the online PnP
algorithm which can adaptively update the network's parameters within the PnP
iteration; this makes the denoising network more applicable to the desired data
in the SCI reconstruction. Furthermore, for color video imaging, RGB frames
need to be recovered from Bayer pattern or named demosaicing in the camera
pipeline. To address this challenge, we design a two-stage reconstruction
framework to optimize these two coupled ill-posed problems and introduce a deep
demosaicing prior specifically for video demosaicing which does not have much
past works instead of using single image demosaicing networks. Extensive
results on both simulation and real datasets verify the superiority of our
adaptive deep PnP algorithm.
