Jointly recognizing OAM mode and compensating wavefront distortion using one convolutional neural network

CHENDA LU, QINGHUA TIAN*, XIANGJUN XIN, BO LIU, QI ZHANG, YONGJUN WANG, FENG TIAN, LEIJING YANG, RAN GAO

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

21 引用 (Scopus)

摘要

In this work, a new recognition method of orbital angular momentum (OAM) is proposed. The method combines mode recognition and the wavefront sensor-less (WFS-less) adaptive optics (AO) by utilizing a jointly trained convolutional neural network (CNN) with the shared model backbone. The CNN-based AO method is implicitly applied in the system by providing additional mode information in the offline training process and accordingly the system structure is rather concise with no extra AO components needed. The numerical simulation result shows that the proposed method can improve the recognition accuracy significantly in different conditions of turbulence and can achieve similar performance compared with AO-combined methods.

源语言英语
页(从-至)37936-37945
页数10
期刊Optics Express
28
25
DOI
出版状态已出版 - 7 12月 2020

指纹

探究 'Jointly recognizing OAM mode and compensating wavefront distortion using one convolutional neural network' 的科研主题。它们共同构成独一无二的指纹。

引用此