基于双光斑的深度学习波前复原及校正  

Double Spots Based Deep Learning Wavefront Reconstruction and Correction

作　　者：许元斋 唐秋艳[3] 王小军 郭亚丁[1] 张林 魏花 彭钦军[1] 吕品 Xu Yuanzhai;Tang Qiuyan;Wang Xiaojun;Guo Yading;Zhang Lin;Wei Hua;Peng Qinjun;LüPin(Technical Institute of Physics and Chemistry,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Software,Chinese Academy of Sciences,Beijing 100191,China;Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院理化技术研究所,北京100190 [2]中国科学院大学,北京100049 [3]中国科学院软件研究所,北京100191 [4]中国科学院自动化研究所,北京100190

出　　处：《中国激光》2024年第13期24-33,共10页Chinese Journal of Lasers

摘　　要：探索了深度学习算法求解光强反演波前的非线性映射,实现了基于深度学习的波前探测(DLWFS)在自适应光学波前校正中的应用。采用聚焦和离焦双光斑反演波前,既简化了探测器结构,又减小了探测器体积。由于光强反演波前的过程在物理上不存在显式解,因此可以利用深度学习模型cGAN,通过类似图像处理的方式,建立光强-相位的非线性映射,将光斑的强度分布反演为波前分布,最终使波前校正系统配备较为紧凑的波前探测器。训练数据集通过物理衍射仿真获得,模型在测试集上的波前复原的最小残差RMS<0.3μm。在实验中,DLWFS与参考哈特曼波前探测器波前的残差RMS在0.0965~0.1531μm之间。在自适应光学波前校正实验中,将DLWFS作为自适应光学闭环校正过程中的波前探测器,验证了DLWFS的实用效果,光束质量因子从10.83校正至3.61。此外,还讨论了DLWFS的参数敏感性。Objective The thermal effects and mechanical deformation of high-power lasers impede the output performance of laser systems.Compact laser systems,such as solid lasers,increasingly rely on adaptive optics (AO) featuring simpler structured wavefront sensors to improve beam quality.Unlike the traditional methods that retrieve wavefront from intensity distribution,deep learning,which is well-suited for nonlinear mapping,holds significant potential in this regard.In this article,we present a deep learning wavefront sensor (DLWFS) and demonstrate its applications in AO wavefront corrections.We use conditional generative adversarial networks(c GAN) to extract high-level features from the entire input intensity and retrieve wavefront from the intensity distribution.In other words,we view this intensity-to-wavefront nonlinear mapping as an image-translating problem.To overcome the compression of the wavefront information due to the diversity of coordinates during focusing propagation with a converged beam,the DLWFS relies on acquiring intensity from both the focal spot and the spot just before the focus,also called“double spots”,as input intensity distribution.By comparing the wavefront reconstruction results of DLWFS with those of commercial Shack-Hartmann wavefront sensor (SHWFS),and applying DLWFS in AO closed-loop of wavefront correction,the practicability of DLWFS can be proved.Methods We simulated the propagation of random initial wavefront through physical diffraction to obtain the intensity of spots on focus and defocus (0.98 times focal length) as training data and testing data of DLWFS.Network model c GAN was constructed by a generator (G) and discriminator (D).G had a U-Net structure comprising encoder-decoder convolutional neural networks (CNNs).It was trained to generate wavefront G (x) from input intensity distribution x (x_1,x_(2)),considering both on focus (x_1) and defocus (x_(2))intensity data.The discriminator with a U-Net structure of encoder-decoder was trained to distinguish between tuple (G (x)

关 键 词：自适应光学 波前复原 深度学习 光束质量 

分 类 号：O436.1[机械工程—光学工程]