We investigate deep learning for video compressive sensing within the scope of snapshot compressive imaging (SCI). In video SCI, multiple high-speed frames are modulated by different coding patterns and then a low-speed detector captures the integration of these modulated frames. In this manner, each captured measurement frame incorporates the information of all the coded frames, and reconstruction algorithms are then employed to recover the high-speed video. In this paper, we build a video SCI system using a digital micromirror device and develop both an end-to-end convolutional neural network (E2E-CNN) and a Plug-and-Play (PnP) framework with deep denoising priors to solve the inverse problem. We compare them with the iterative baseline algorithm GAP-TV and the state-of-the-art DeSCI on real data. Given a determined setup, a well-trained E2E-CNN can provide video-rate high-quality reconstruction. The PnP deep denoising method can generate decent results without task-specific pre-training and is faster than conventional iterative algorithms. Considering speed, accuracy, and flexibility, the PnP deep denoising method may serve as a baseline in video SCI reconstruction. To conduct quantitative analysis on these reconstruction algorithms, we further perform a simulation comparison on synthetic data. We hope that this study contributes to the applications of SCI cameras in our daily life.

中文翻译：

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用于视频压缩感测的深度学习

我们调查快照压缩成像（SCI）范围内的视频压缩感测的深度学习。在视频SCI中，多个高速帧通过不同的编码模式进行调制，然后低速检测器捕获这些调制帧的积分。以这种方式，每个捕获的测量帧都合并了所有编码帧的信息，然后采用重建算法来恢复高速视频。在本文中，我们使用数字微镜设备构建了一个视频SCI系统，并开发了端到端卷积神经网络（E2E-CNN）和即插即用（PnP）框架，并具有深度去噪先验以解决该问题。反问题。我们将它们与迭代基线算法GAP-TV和最新的DeSCI进行了实数据比较。有了确定的设置，训练有素的E2E-CNN可以提供视频速率的高质量重建。PnP深度降噪方法无需进行特定于任务的预训练即可生成不错的结果，并且比传统的迭代算法更快。考虑到速度，准确性和灵活性，PnP深度降噪方法可以用作视频SCI重建的基线。为了对这些重建算法进行定量分析，我们进一步对合成数据进行了仿真比较。我们希望这项研究有助于SCI相机在我们日常生活中的应用。PnP深度降噪方法可以作为视频SCI重建的基线。为了对这些重建算法进行定量分析，我们进一步对合成数据进行了模拟比较。我们希望这项研究有助于SCI相机在我们日常生活中的应用。PnP深度降噪方法可以作为视频SCI重建的基线。为了对这些重建算法进行定量分析，我们进一步对合成数据进行了模拟比较。我们希望这项研究有助于SCI相机在我们日常生活中的应用。