(Peer-Reviewed) Adaptive optics based on machine learning: a review
Youming Guo 郭友明 ¹ ² ³, Libo Zhong 钟立波 ¹ ², Lei Min 闵雷 ¹ ², Jiaying Wang 王佳英 ¹ ² ³, Yu Wu ¹ ² ³, Kele Chen 陈克乐 ¹ ² ³, Kai Wei 魏凯 ¹ ² ³, Changhui Rao 饶长辉 ¹ ² ³
¹ The Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, China
中国 成都 中国科学院 自适应光学重点实验室
² The Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
中国 成都 中国科学院 光电技术研究所 自适应光学实验室
³ University of Chinese Academy of Sciences, Beijing 100049, China
中国 北京 中国科学院大学
Opto-Electronic Advances, 2022-01-28
Abstract
Adaptive optics techniques have been developed over the past half century and routinely used in large ground-based telescopes for more than 30 years. Although this technique has already been used in various applications, the basic setup and methods have not changed over the past 40 years. In recent years, with the rapid development of artificial intelligence, adaptive optics will be boosted dramatically.
In this paper, the recent advances on almost all aspects of adaptive optics based on machine learning are summarized. The state-of-the-art performance of intelligent adaptive optics are reviewed. The potential advantages and deficiencies of intelligent adaptive optics are also discussed.
Separation and identification of mixed signal for distributed acoustic sensor using deep learning
Huaxin Gu, Jingming Zhang, Xingwei Chen, Feihong Yu, Deyu Xu, Shuaiqi Liu, Weihao Lin, Xiaobing Shi, Zixing Huang, Xiongji Yang, Qingchang Hu, Liyang Shao
Opto-Electronic Advances
2025-11-25
