High-fidelity image reconstruction for compressed ultrafast photography via an augmented-Lagrangian and deep-learning hybrid algorithm

Chengshuai Yang; Yunhua Yao; Chengzhi Jin; Dalong Qi; Fengyan Cao; Yilin He; Jiali Yao; Pengpeng Ding; Liang Gao; Tianqing Jia; Jinyang Liang; Zhenrong Sun; Shian Zhang

doi:10.1364/PRJ.410018

Photonics Research, Volume. 9, Issue 2, B30(2021)

High-fidelity image reconstruction for compressed ultrafast photography via an augmented-Lagrangian and deep-learning hybrid algorithm

Chengshuai Yang¹, Yunhua Yao^1,6、*, Chengzhi Jin¹, Dalong Qi¹, Fengyan Cao¹, Yilin He¹, Jiali Yao¹, Pengpeng Ding¹, Liang Gao², Tianqing Jia¹, Jinyang Liang³, Zhenrong Sun¹, and Shian Zhang^1,4,5,7、*

Author Affiliations

¹State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China

²Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA

³Institut National de la Recherche Scientifique, Centre Énergie Matériaux Télécommunications, Laboratory of Applied Computational Imaging, Varennes, Québec J3X1S2, Canada

⁴Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China

⁵Collaborative Innovation Center of Light Manipulations and Applications, Shandong Normal University, Jinan 250358, China

⁶e-mail: yhyao@lps.ecnu.edu.cn

⁷e-mail: sazhang@phy.ecnu.edu.cn

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Chengshuai Yang, Yunhua Yao, Chengzhi Jin, Dalong Qi, Fengyan Cao, Yilin He, Jiali Yao, Pengpeng Ding, Liang Gao, Tianqing Jia, Jinyang Liang, Zhenrong Sun, Shian Zhang, "High-fidelity image reconstruction for compressed ultrafast photography via an augmented-Lagrangian and deep-learning hybrid algorithm," Photonics Res. 9, B30 (2021)

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Paper Information

Special Issue: DEEP LEARNING IN PHOTONICS

Received: Sep. 15, 2020

Accepted: Dec. 2, 2020

Published Online: Jan. 22, 2021

The Author Email: Yunhua Yao (yhyao@lps.ecnu.edu.cn), Shian Zhang (sazhang@phy.ecnu.edu.cn)

DOI:10.1364/PRJ.410018

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