  Snapshot compressive imaging (SCI) captures high-dimensional data efficiently
by compressing it into two-dimensional observations and reconstructing
high-dimensional data from two-dimensional observations with various
algorithms. Plug-and-play (PnP) is a promising approach for the video SCI
reconstruction because it can leverage both the observation model and denoising
methods for videos. This paper proposes a deep unfolding-based method for
tuning noise level parameters in PnP-based video SCI, which significantly
affects the reconstruction accuracy. For the training of the parameters, we
prepare training data from the densely annotated video segmentation (DAVIS)
dataset, reparametrize the noise level parameters, and apply the checkpointing
technique to reduce the required memory. Simulation results show that the
trained noise level parameters significantly improve the reconstruction
accuracy and exhibit a non-monotonic pattern, which is different from the
assumptions in the conventional convergence analyses of PnP-based algorithms.
