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
Fig. 1. Data flow chart of the
Fig. 2. (a) U-net architecture in the
Fig. 3. Reconstructed results of (a) boatman, (b) ocean animal, and (c) finger by the
Fig. 4. System configuration of CUP. DMD, digital micromirror device; CMOS, complementary metal–oxide-semiconductor.
Fig. 5. Measuring temporal evolution of a spatially modulated picosecond laser spot. (a) Experimental design. (b)–(d) Reconstructed results by the
Fig. 6. Measuring wavefront movement by obliquely illuminating a collimated femtosecond laser pulse on a transverse fan pattern. (a) Experimental design. (b)–(d) Reconstructed results by the
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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)
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)