Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network

TL;DR

This paper introduces an optical flow-aided recurrent neural network that efficiently reconstructs dual-view videos from snapshot compressive imaging data, outperforming traditional methods in speed and quality.

Contribution

It proposes a novel deep learning framework combining optical flow and recurrent neural networks for dual-view SCI, enabling fast and high-quality scene separation and reconstruction.

Findings

Achieves high-quality reconstruction in seconds.

Outperforms existing algorithms in accuracy and speed.

Validated on both simulated and real data.

Abstract

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.