Abstract

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.

© 2021 Optical Society of America

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