Compressed ultrafast photography (CUP) is the fastest single-shot passive ultrafast optical imaging technique, which has shown to be a powerful tool in recording self-luminous or non-repeatable ultrafast phenomena. However, the low fidelity of image reconstruction based on the conventional augmented-Lagrangian (AL) and two-step iterative shrinkage/thresholding (TwIST) algorithms greatly prevents practical applications of CUP, especially for those ultrafast phenomena that need high spatial resolution. Here, we develop a novel AL and deep-learning (DL) hybrid (i.e.,
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[J]. Photonics Research, 2021, 9(2): B30