  Dual-view snapshot compressive imaging (SCI) aims to capture videos from two
field-of-views (FoVs) using a 2D sensor (detector) in a single snapshot,
achieving joint FoV and temporal compressive sensing, and thus enjoying the
advantages of low-bandwidth, low-power, and low-cost. However, it is
challenging for existing model-based decoding algorithms to reconstruct each
individual scene, which usually require exhaustive parameter tuning with
extremely long running time for large scale data. In this paper, we propose an
optical flow-aided recurrent neural network for dual video SCI systems, which
provides high-quality decoding in seconds. Firstly, we develop a diversity
amplification method to enlarge the differences between scenes of two FoVs, and
design a deep convolutional neural network with dual branches to separate
different scenes from the single measurement. Secondly, we integrate the
bidirectional optical flow extracted from adjacent frames with the recurrent
neural network to jointly reconstruct each video in a sequential manner.
Extensive results on both simulation and real data demonstrate the superior
performance of our proposed model in a short inference time. The code and data
are available at https://github.com/RuiyingLu/OFaNet-for-Dual-view-SCI.
