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
Abstract
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.