Huang Honghao, Teng Jiajie, Liang Yu, Hu Chengyang, Chen Minghua, Yang Sigang, Chen Hongwei
Opt Express. 2022 Oct 10;30(21):39111-39128. doi: 10.1364/OE.471754.
Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition. Numerous algorithms, ranging from regularization-based optimization and deep learning, are being investigated to improve reconstruction quality, but they are still limited by the ill-posed and information-deficient nature of the standard SCI paradigm. To overcome these drawbacks, we propose a new key frames assisted hybrid encoding paradigm for compressive video sensing, termed KH-CVS, that alternatively captures short-exposure key frames without coding and long-exposure encoded compressive frames to jointly reconstruct high-quality video. With the use of optical flow and spatial warping, a deep convolutional neural network framework is constructed to integrate the benefits of these two types of frames. Extensive experiments on both simulations and real data from the prototype we developed verify the superiority of the proposed method.
快照压缩成像（SCI）将高速场景视频编码为一个快照测量值，然后通过计算进行重建，从而实现高效的高维数据采集。为了提高重建质量，人们正在研究许多算法，从基于正则化的优化算法到深度学习算法，但它们仍然受到标准SCI范式不适定和信息不足性质的限制。为了克服这些缺点，我们提出了一种用于压缩视频传感的新的关键帧辅助混合编码范式，称为KH-CVS，它交替捕获未编码的短曝光关键帧和长曝光编码压缩帧，以联合重建高质量视频。通过使用光流和空间扭曲，构建了一个深度卷积神经网络框架，以整合这两种类型帧的优点。在我们开发的原型的模拟和真实数据上进行的大量实验验证了所提方法的优越性。
