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Deep-learning-based image reconstruction for compressed ultrafast photography

Optics Letters
  • Yayao Ma, Xiaohua Feng, and Liang Gao
  • received 05/15/2020; accepted 06/26/2020; posted 06/26/2020; Doc. ID 397717
  • Abstract: Compressed ultrafast photography (CUP) is a computational optical imaging technique that can capture transient dynamics at an unprecedented speed. Currently, the image reconstruction of CUP relies on iterative algorithms, which are time-consuming and often yield non-optimal image quality. To solve this problem, we develop a deep-learning-based method for CUP reconstruction that substantially improves the image quality and reconstruction speed. A key innovation towards efficient DL reconstruction of a large three-dimensional (3D) event datacube (x, y, t) (x, y, spatial coordinate; t, time) is that we decompose the original datacube into massively-parallel two-dimensional (2D) imaging sub-problems, which are much simpler to solve by a deep neural network. We validated our approach on simulated and experimental data.
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