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基于远场信息和卷积神经网络的波前重构方法

史宗佳 向振佼 杜应磊 万敏 顾静良 李国会 向汝建 游疆 吴晶 徐宏来

史宗佳, 向振佼, 杜应磊, 等. 基于远场信息和卷积神经网络的波前重构方法[J]. 强激光与粒子束, 2021, 33: 081011. doi: 10.11884/HPLPB202133.210040
引用本文: 史宗佳, 向振佼, 杜应磊, 等. 基于远场信息和卷积神经网络的波前重构方法[J]. 强激光与粒子束, 2021, 33: 081011. doi: 10.11884/HPLPB202133.210040
Shi Zongjia, Xiang Zhenjiao, Du Yinglei, et al. Wavefront reconstruction method based on far-field information and convolutional neural network[J]. High Power Laser and Particle Beams, 2021, 33: 081011. doi: 10.11884/HPLPB202133.210040
Citation: Shi Zongjia, Xiang Zhenjiao, Du Yinglei, et al. Wavefront reconstruction method based on far-field information and convolutional neural network[J]. High Power Laser and Particle Beams, 2021, 33: 081011. doi: 10.11884/HPLPB202133.210040

基于远场信息和卷积神经网络的波前重构方法

doi: 10.11884/HPLPB202133.210040
基金项目: 中国工程物理研究院创新发展基金项目(CX2020033);国防科技创新特区课题项目(193A221011101)
详细信息
    作者简介:

    史宗佳(1995—),男,硕士研究生,从事自适应光学方面的研究

    通讯作者:

    杜应磊(1988—),男,学士,助理研究员,从事激光系统主动光学技术研究

  • 中图分类号: O439

Wavefront reconstruction method based on far-field information and convolutional neural network

  • 摘要: 探测波前相位信息是实现自适应光学波前补偿的关键,使用卷积神经网络(CNN)代替波前传感器进行波前重构,系统简单易于实现,同时重构过程不依赖迭代运算,快速实时。为准确提取远场中的波前特征,CNN需要事先使用大量样本进行训练。研究中根据4~30阶大气湍流泽尼克像差系数与其远场强度的对应关系,仿真制作样本数据集,训练CNN从输入的一帧远场图像中预测出畸变波前的泽尼克像差系数,重构原始波前。验证结果表明,该方法能快速实时地还原出波前相位信息,重构波前较原始波前具有极高的波面吻合度和较小的残差剩余量,有望实现实际自适应光学系统中的闭环校正。
  • 图  1  基于CNN的波前重构系统

    Figure  1.  Wavefront reconstruction system based on CNN

    图  2  不同的残差单元

    Figure  2.  Different residual elements

    图  3  CNN波前重构流程图

    Figure  3.  Flow chart of CNN wavefront reconstruction

    图  4  某一样本远场和波前泽尼克系数

    Figure  4.  Far-field and wavefront Zernike coefficients of a sample

    图  5  训练中四种ResNet模型的L1 loss变化过程,(a)图为训练集,(b)图为验证集

    Figure  5.  The L1 loss change process of the four ResNet models in training is shown in (a) the training set and (b) the verification set

    图  6  网络的单帧图像预测时间

    Figure  6.  Single frame image prediction time of ResNet

    图  7  测试集中某一样本的波前重构结果

    Figure  7.  Wavefront reconstruction results of a sample in the test set

    图  8  测试集样本原始波前和波前残差PV和RMS的散点图

    Figure  8.  Scatter plot of PV and RMS of original wavefront and wavefront residuals of test set samples

    图  9  测试集样本波前残差与原始波前的PV和RMS比值

    Figure  9.  Ratio of PV and RMS of sample wavefront residuals to original wavefront of test set

    表  1  不同强度湍流的重构波前结果

    Table  1.   Wavefront reconstruction results of turbulence with different intensities

    R0far field
    image size/
    pixel
    L1
    error
    normalized
    coefficient
    RMSE
    PV of the
    test set samples’
    original
    wavefront/μm
    RMS of the
    test set samples’
    original
    wavefront/μm
    PV of
    reconstructed
    wavefront
    residuals/μm
    RMS of
    reconstructed
    wavefront
    residuals/μm
    residual PV
    to original
    wavefront ratio
    (90% of sample)/%
    residual RMS
    to original
    wavefront ratio
    (90% of sample)/%
    1140×1400.00400.00512.67±1.630.54±0.370.12±0.070.02±0.0165
    0.5200×2000.02040.02665.06±2.761.0±0.551.14±0.750.20±0.133027
    下载: 导出CSV
  • [1] 周仁忠. 自适应光学[J]. 中国光学, 1997(5):98-99. (Zhou Renzhong. Adaptive optics[J]. Optics of China, 1997(5): 98-99
    [2] Hardy J W. Adaptive optics: a progress review[C]//Proceedings of SPIE Active and Adaptive Optical Systems. San Diego, CA, USA: SPIE, 1991: 1542.
    [3] Yasuno Y, Wiesendanger T F, Ruprecht A K, et al. Wavefront-flatness evaluation by wavefront-correlation-information-entropy method and its application for adaptive confocal microscope[J]. Optics Communications, 2004, 232(1/6): 91-97.
    [4] 母杰, 景峰, 王逍, 等. 相干合成中基于SPGD算法的平移误差和倾斜误差控制[J]. 中国激光, 2014, 41:0602002. (Mu Jie, Jing Feng, Wang Xiao, et al. Error control of piston and tilt based on SPGD in coherent beam combination[J]. Chinese Journal of Lasers, 2014, 41: 0602002 doi: 10.3788/CJL201441.0602002
    [5] Vorontsov M A, Carhart G W, Ricklin J C. Adaptive phase-distortion correction based on parallel gradient-descent optimization[J]. Optics Letters, 1997, 22(12): 907-909. doi: 10.1364/OL.22.000907
    [6] Débarre D, Booth M J, Wilson T. Image based adaptive optics through optimisation of low spatial frequencies[J]. Optics Express, 2007, 15(13): 8176-8190. doi: 10.1364/OE.15.008176
    [7] Kendrick R L, Acton D S, Duncan A L. Phase-diversity wave-front sensor for imaging systems[J]. Applied Optics, 1994, 33(27): 6533-6546. doi: 10.1364/AO.33.006533
    [8] Guo Hong, Korablinova N, Ren Qiushi, et al. Wavefront reconstruction with artificial neural networks[J]. Optics Express, 2006, 14(14): 6456-6462. doi: 10.1364/OE.14.006456
    [9] Nguyen T, Bui V, Lam V, et al. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection[J]. Optics Express, 2017, 25(13): 15043-15057. doi: 10.1364/OE.25.015043
    [10] Paine S W, Fienup J R. Machine learning for improved image-based wavefront sensing[J]. Optics Letters, 2018, 43(6): 1235-1238. doi: 10.1364/OL.43.001235
    [11] Nishizaki Y, Valdivia M, Horisaki R, et al. Deep learning wavefront sensing[J]. Optics Express, 2019, 27(1): 240-251. doi: 10.1364/OE.27.000240
    [12] Tian Qinghua, Lu Chenda, Liu Bo, et al. DNN-based aberration correction in a wavefront sensorless adaptive optics system[J]. Optics Express, 2019, 27(8): 10765-10776. doi: 10.1364/OE.27.010765
    [13] 马慧敏, 焦俊, 乔焰, 等. 一种基于光强图像深度学习的波前复原方法[J]. 激光与光电子学进展, 2020, 57:081103. (Ma Huimin, Jiao Jun, Qiao Yan, et al. Wavefront restoration method based on light intensity image deep learning[J]. Laser & Optoelectronics Progress, 2020, 57: 081103
    [14] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
    [15] 徐瑞超, 高明. 大气湍流等效相位屏的仿真研究[J]. 西安工业大学学报, 2018, 38(2):108-113. (Xu Ruichao, Gao Ming. Simulation of the equivalent phase screen distorted by atmospheric turbulence[J]. Journal of Xi'an Technological University, 2018, 38(2): 108-113
    [16] Yan Haixing, Li Shushan, Zhang Deliang, et al. Numerical simulation of an adaptive optics system with laser propagation in the atmosphere[J]. Applied Optics, 2000, 39(18): 3023-3031. doi: 10.1364/AO.39.003023
    [17] Lane R G, Glindemann A, Dainty J C. Simulation of a Kolmogorov phase screen[J]. Waves in Random Media, 1992, 2(3): 209-224. doi: 10.1088/0959-7174/2/3/003
    [18] Yang Ping, Ao Mingwu, Liu Yuan, et al. Intracavity transverse modes controlled by a genetic algorithm based on Zernike mode coefficients[J]. Optics Express, 2007, 15(25): 17051-17062. doi: 10.1364/OE.15.017051
    [19] 粘伟, 刘兆军, 李博. 大口径空间望远镜变形镜校正能力分析[J]. 科学技术与工程, 2018, 18(23):219-223. (Nian Wei, Liu Zhaojun, Li Bo. Correction quality analysis of deformable mirror for large aperture space telescope[J]. Science Technology and Engineering, 2018, 18(23): 219-223 doi: 10.3969/j.issn.1671-1815.2018.23.030
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出版历程
  • 收稿日期:  2021-02-04
  • 修回日期:  2021-04-02
  • 网络出版日期:  2021-04-19
  • 刊出日期:  2021-08-15

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