  Video snapshot compressive imaging (SCI) uses a two-dimensional detector to
capture consecutive video frames during a single exposure time. Following this,
an efficient reconstruction algorithm needs to be designed to reconstruct the
desired video frames. Although recent deep learning-based state-of-the-art
(SOTA) reconstruction algorithms have achieved good results in most tasks, they
still face the following challenges due to excessive model complexity and GPU
memory limitations: 1) these models need high computational cost, and 2) they
are usually unable to reconstruct large-scale video frames at high compression
ratios. To address these issues, we develop an efficient network for video SCI
by using dense connections and space-time factorization mechanism within a
single residual block, dubbed EfficientSCI. The EfficientSCI network can well
establish spatial-temporal correlation by using convolution in the spatial
domain and Transformer in the temporal domain, respectively. We are the first
time to show that an UHD color video with high compression ratio can be
reconstructed from a snapshot 2D measurement using a single end-to-end deep
learning model with PSNR above 32 dB. Extensive results on both simulation and
real data show that our method significantly outperforms all previous SOTA
algorithms with better real-time performance. The code is at
https://github.com/ucaswangls/EfficientSCI.git.
