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 {\bf{\em efficient network}} for video
SCI by using {\bf {\em dense connections and space-time factorization
mechanism}} within a single residual block, dubbed {\bf \emph{EfficientSCI}}.
The EfficientSCI network can well establish spatial-temporal correlation by
using {\bf {\em 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.

本文提出了一种名为 EfficientSCI 的高效深度学习网络，使用含有密集连接和时空分解机制的单个残差块，通过卷积和 Transformer 等操作，在单一暴光时间内重建视频快照，从而解决了过度模型复杂性和 GPU 内存限制等问题，实验结果表明该算法可以显著优于当前所有先进技术算法，PSNR 值高达 32 dB 以上。