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基于深度学习的自适应光学波前传感技术研究综述

李自强 李新阳 高泽宇 贾启旺

李自强, 李新阳, 高泽宇, 等. 基于深度学习的自适应光学波前传感技术研究综述[J]. 强激光与粒子束, 2021, 33: 081001. doi: 10.11884/HPLPB202133.210158
引用本文: 李自强, 李新阳, 高泽宇, 等. 基于深度学习的自适应光学波前传感技术研究综述[J]. 强激光与粒子束, 2021, 33: 081001. doi: 10.11884/HPLPB202133.210158
Li Ziqiang, Li Xinyang, Gao Zeyu, et al. Review of wavefront sensing technology in adaptive optics based on deep learning[J]. High Power Laser and Particle Beams, 2021, 33: 081001. doi: 10.11884/HPLPB202133.210158
Citation: Li Ziqiang, Li Xinyang, Gao Zeyu, et al. Review of wavefront sensing technology in adaptive optics based on deep learning[J]. High Power Laser and Particle Beams, 2021, 33: 081001. doi: 10.11884/HPLPB202133.210158

基于深度学习的自适应光学波前传感技术研究综述

doi: 10.11884/HPLPB202133.210158
基金项目: 国家自然科学基金项目(62005286)
详细信息
    作者简介:

    李自强(1994—),男,博士,博士后,从事自适应光学波前传感及智能控制的研究

    通讯作者:

    李新阳(1971—),男,博士,研究员,博士生导师,从事自适应光学、激光技术等多方面的研究工作

  • 中图分类号: O43; TP18

Review of wavefront sensing technology in adaptive optics based on deep learning

  • 摘要:

    波前传感是自适应光学系统的重要组成部分,在地基大口径望远镜、激光大气传输、无线光通信、激光驱动核聚变等领域发挥了关键作用,同时也常应用于自由曲面的光学测量中。与此同时,深度学习作为一种较为通用的前沿技术,成功在计算机视觉、自然语言处理等众多领域取得了革命性进展。使用深度学习的方法改进自适应光学系统中的波前传感器,以期实现更精准的波前探测,以及适应更复杂的应用场景是自适应光学的发展趋势,也是深度学习应用领域的一个新课题。介绍了深度学习在自适应光学波前传感中的应用现状,主要分析了在相位反演波前传感器和哈特曼波前传感器中的研究特点,并在最后进行了总结和展望。

  • 图  2  神经网络及神经元的运算

    Figure  2.  ANN and the basic operation of a neuron

    图  1  深度学习发展历程

    Figure  1.  Development of Deep Learning

    图  3  使用神经网络波前传感器校正MMT两块独立镜面的像差的光路图[19]

    Figure  3.  Optical path of using wavefront sensor based on ANN to correct the aberration of two independent mirrors of MMT

    图  4  一种神经网络波前传感器的网络架构[20]

    Figure  4.  Architecture of wavefront sensor based on ANN

    图  5  PD-CNN同时输入焦面图像和离焦图像的测试结果

    Figure  5.  Result of the PD model of PD-CNN

    图  6  (a)是实验验证概念图,(b)是实验实物图[26]

    Figure  6.  Sketch (a) and physical map (b) of the optical system used in the experiment

    图  7  哈特曼波前传感器中的拉长效应。其中r是子孔径到中心的距离,h0是钠层的平均高度,σNA是钠层的厚度,z是天顶角[33]

    Figure  7.  Elongation of spots in the SHWFS. Here, r is the distance of the subaperture in the SHWFS from the center (as projected on to the primary mirror), h0 is the average altitude of the sodium layer, σNA is the thickness of the sodium layer and z is the zenith angle

    图  8  基于神经网络SHNN对哈特曼传感器在强光干扰下进行质心探测的实验结果[42]

    Figure  8.  Experimental result of centroid computation for Shack-Hartmann wavefront sensor in extreme situations based on the SHNN

    图  9  使用多个激光导引星的波前探测

    Figure  9.  Wavefront sensing by using multiple laser guide stars

    图  10  全连接卡门示意图[47]

    Figure  10.  CARMEN with MLP

    图  11  卷积卡门的网络拓扑结构示意图[55]

    Figure  11.  Example of the topology of CARMEN with CNN

    图  12  ISNet的网络结构图[59]

    Figure  12.  ISNet architecture

    图  13  SH-Net、LSHWS、Swanson的网络、模式法以及区域法的仿真测试统计结果,每种不同的相位屏都包含100个数据[60]

    Figure  13.  Statistical results of the RMS wavefront error of five methods in wavefront detection. Each kind of phase screen contains 100 datasets

    图  14  CS-WFS的物理过程和计算流程图[62]

    Figure  14.  Physical process and flow chart of the CS-WFS method

    表  1  实验结果

    Table  1.   Results of experiments

    MethodsPerformances
    CEE/pixelFalse RatePV/umRMS/um
    TCoG4.595895.31%2.65930.5349
    SHNN-500.52501.17%0.31070.0651
    下载: 导出CSV
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  • 收稿日期:  2021-04-22
  • 修回日期:  2021-07-08
  • 网络出版日期:  2021-07-26
  • 刊出日期:  2021-08-15

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