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 short inference time. The code and data are available at https://github.com/RuiyingLu/OFaNet-for-Dual-view-SCI.

双视角快照压缩成像 (SCI) 旨在在单个快照中使用 2D 传感器 (探测器) 从两个视场 (FoV) 捕获视频，实现 FoV 和时间压缩感知的联合，从而享受低- 带宽、低功耗和低成本。然而，现有的基于模型的解码算法很难重建每个单独的场景，对于大规模数据，这通常需要详尽的参数调整和极长的运行时间。在本文中，我们提出了一种用于双视频 SCI 系统的光流辅助循环神经网络，可在几秒钟内提供高质量的解码。首先，我们开发了一种多样性放大方法来扩大两个 FoV 场景之间的差异，并设计一个具有双分支的深度卷积神经网络，将不同场景与单次测量分开。其次，我们将从相邻帧中提取的双向光流与循环神经网络相结合，以顺序方式联合重建每个视频。模拟和真实数据的广泛结果证明了我们提出的模型在较短的推理时间内的优越性能。代码和数据可在 https://github.com/RuiyingLu/OFaNet-for-Dual-view-SCI 获得。