基于深度相位估计网络的涡旋光束相位校正  

Vortex Beam Phase Correction Based on Deep Phase Estimation Network

作　　者：刘娟 杜乾 刘芳宁 王珂 余佳益 魏冬梅[1] Liu Juan;Du Qian;Liu Fangning;Wang Ke;Yu Jiayi;Wei Dongmei(Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and Electronics,Shandong Normal University Jinan 250358)

机构地区：[1]山东师范大学物理与电子科学学院光场调控及应用中心,济南250358

出　　处：《光学学报》2023年第6期171-179,共9页Acta Optica Sinica

基　　金：国家自然科学基金面上项目(42271093);国家自然科学基金青年基金(12004218);山东省本科教学改革项目(2021BJ055)。

摘　　要：大气湍流会引起涡旋光束(VB)的相位畸变和模态扩散,造成湍流介质中通信性能下降。提出基于深度相位估计网络的拉盖尔-高斯(LG)光束相位补偿方法,以提高模态检测准确度,即通信的可靠性。分别利用使用和不使用高斯光作为探测光束两种方案进行学习,在光强图像与大气湍流引起的扰动相位之间建立映射关系,在接收端依据接收到的光强图像,预测湍流引起的扰动相位并进行相位校正。结果表明,所提深度网络能够较准确地实现相位预测,经补偿后,光束模态纯度达到95%以上,补偿后光强图像与发送端光强图像之间的均方误差明显减小。Objective The vortex beam carries orbital angular momentum and has a phase factor exp()ilq,where l is the topological charge number.Theoretically,l can take any integer value,and different orbital angular momentum modes are mutually orthogonal.Therefore,in optical communication,the orbital angular momentum can be used for information transmission and exchange,or the orbital angular momentum can be multiplexed to improve communication capacity.However,vortex beams are affected by turbulence when transmitted in atmospheric turbulence,resulting in distortion of their spiral phase,causing inter-mode crosstalk and causing reduced communication reliability.There are many works dedicated to compensate the phase distortion of vortex beams,and the commonly used method is the adaptive optics system.However,such methods require multiple iterations,converge slowly,and easily fall into local minima.In recent years convolutional neural networks have attracted a lot of attention in various fields due to their powerful image processing capabilities.Therefore,in this paper,convolutional neural networks are used to extract atmospheric turbulence information from distorted light intensity distribution and recover its distortion.This deep learning-based compensation method has even more accurate and faster correction capability than adaptive optics systems.To this end,this paper proposes to implement the prediction of atmospheric turbulence phase using convolutional neural networks to achieve Laguerre-Gaussian beam phase compensation and improve the accuracy of modal detection and the reliability of communication.Methods In this paper,a novel convolutional neural network,deep phase estimation network,is constructed to achieve the prediction of turbulent phases.Using this proposed deep network,a mapping between intensity and turbulence phase caused by atmospheric turbulence is established.Here two strategies are used for learning and prediction turbulence phase respectively,one is using Gaussian beam as probe beam,the other is not us

关 键 词：大气光学 涡旋光束 卷积神经网络 相位预测 大气湍流 

分 类 号：TN929.12[电子电信—通信与信息系统]