摘要
针对大气湍流环境下光学元件平面面形PV值测量这一问题。首先建立了一种大气湍流下斐索干涉仪的模型,通过该模型得到1000张干涉条纹图像;然后提出了一种基于卷积神经网络估算PV值的方法,将干涉条纹图像作为卷积神经网络的输入,利用卷积神经网络提取图像的特征信息,得到PV值;最后将得到的结果与ASTM计算得到的结果、相位解包裹得到的结果以及BP神经网络得到的结果进行对比,发现利用卷积神经网络的方法偏差为2.25×10^(-4)λ,较ASTM、相位解包裹以及BP神经网络得到的结果偏差更小。实验结果表明此方法具有抗干扰性强、精度高、运算快的优点,是一种有效的抗大气湍流影响的光学检测方法。
Given the task of quantifying the PV value of the plane shape of an optical element under atmospheric turbulence.Initially,a model of the Fizeau interferometer under atmospheric turbulence is developed,through which l,ooo interference fringe images are obtained.What's more,a method of estimating PV value based on convolutional neural network is proposed,leting the interference fringes image as the input of convolutional neural network and extracting the feature information from the image to obtain PV value.Finally,the obtained results are compared with those obtained by ASTM(American Society of Testing Materials)calculation,phase unwrapping and BP(Back Propagation)neural network,and the deviation using the method of convolutional neural network is 2.25×10^(-4)λ,which is more smaller than the results obtained by ASTM,phase unwrapping and BP neural network.The experimental results show that the method has strong interference resistance,high precision and fast operation,and is an effective optical detection method against atmospheric turbulence.
作者
李琳
刘永辉
LI Lin;LIU Yonghui(School of Optical-Electronical Information and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《光学技术》
CAS
CSCD
北大核心
2023年第6期704-710,共7页
Optical Technique
基金
国家自然科学基金项目(61673277)
上海理工大学横向科研项目(H-2021-302-129)。
关键词
大气湍流
平面面形测量
卷积神经网络
斐索干涉仪
atmospheric turbulence
plane measurement
convolutional neural network
Fizeau interferomete
