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

EfficientSCI: Densely Connected Network with Space-time Factorization for Large-scale Video Snapshot Compressive Imaging