Snapshot temporal compressive microscopy using an iterative algorithm with untrained neural networks

Mu Qiao, Xuan Liu, Xin Yuan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1888-1891
Number of pages4
JournalOptics Letters
Volume46
Issue number8
DOIs
StatePublished - Apr 15 2021

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics

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