Ultrafast imaging using spatiotemporal encoding and deep learning

Chen Zhou; Lidan Zhang; Tunan Xia; Cheng-Yu Wang; Xingjie Ni; Zhiwen Liu

doi:10.1117/12.2679921

6 October 2023 Ultrafast imaging using spatiotemporal encoding and deep learning

Chen Zhou, Lidan Zhang, Tunan Xia, Cheng-Yu Wang, Xingjie Ni, Zhiwen Liu

Proceedings Volume PC12681, Ultrafast Nonlinear Imaging and Spectroscopy XI; PC126810G (2023) https://doi.org/10.1117/12.2679921
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

We present a compact, spatiotemporally encoded, deep learning-enabled, single-shot ultrafast imaging system. We simulate the forward light-transport process of an ultrafast event encoded by a random spatiotemporal encoding mask and construct a U-net-based deep learning model to reconstruct the ultrafast event sequences. Trained on simulated ultrafast events consisting of various geometric shapes and handwritten digits with random locations and speeds, the deep learning model can reconstruct multi-frame ultrafast event sequences from simulated single-shot measurements by a normal camera with high reconstruction accuracy and noise tolerance. We also present preliminary fabrication results of spatiotemporal masks. This work provides a simple, cost-effective, single-shot method for studying nonrepetitive ultrafast transient processes.

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Chen Zhou, Lidan Zhang, Tunan Xia, Cheng-Yu Wang, Xingjie Ni, and Zhiwen Liu "Ultrafast imaging using spatiotemporal encoding and deep learning", Proc. SPIE PC12681, Ultrafast Nonlinear Imaging and Spectroscopy XI, PC126810G (6 October 2023); https://doi.org/10.1117/12.2679921

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KEYWORDS

Ultrafast imaging

Deep learning

Device simulation

Ultrafast measurement systems

Ultrafast phenomena

Cameras

Education and training

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