Opt Lett. 2021 Apr 15;46(8):1888-1891. doi: 10.1364/OL.420139.
We report a snapshot temporal compressive microscopy imaging system, using the idea of video compressive sensing, to capture high-speed microscopic scenes with a low-speed camera. An untrained deep neural network is used in our iterative inversion algorithm to reconstruct 20 high-speed video frames from a single compressed measurement. Specifically, using a camera working at 50 frames per second (fps) to capture the measurement, we can recover videos at 1000 fps. Our deep neural network is embedded in the inversion algorithm, and its parameters are learned simultaneously with the reconstruction.
我们报告了一种快照时频压缩显微镜成像系统，利用视频压缩感知的思想，使用低速相机捕捉高速微观场景。在我们的迭代反演算法中，使用未训练的深度神经网络从单个压缩测量中重建 20 个高速视频帧。具体来说，使用工作帧率为 50 帧每秒（fps）的相机来捕获测量值，我们可以以 1000 fps 的帧率恢复视频。我们的深度神经网络被嵌入到反演算法中，其参数与重建过程同时学习。
