基于深度学习的波前复原方法是利用训练好的卷积神经网络(CNN)模型并直接根据输入的光强图像得到波前像差的Zernike系数,不需要进行迭代计算,方法简单易于实现,便于快速获取相位。CNN的训练是通过对大量畸变远场光强图像和其对应的Zernike波前系数数据进行训练,自动提取光强图像特征,学习光强和Zernike系数的关系。本研究基于35阶Zernike大气湍流像差,建立了基于CNN的波前复原模型,通过分析该方法对静态波前畸变的复原能力,验证了基于CNN的波前复原方法的可行性及复原能力。

Wavefront restoration based on deep learning is to obtain Zernike coefficients of wavefront aberration directly from the input light intensity image using the trained convolutional neural network (CNN) model. This method has many advantages, such as without iterative calculation, simple and easy to implement, and easy to quickly obtain phase. The training of CNN is carried out by training a large number of light intensity images of distorted far field and their corresponding Zernike wavefront coefficients, automatically extracting the characteristics of light intensity images, and learning the relationship between light intensity and Zernike coefficients. In this paper, a CNN-based wavefront restoration model is established based on the 35-order Zernike-atmospheric turbulence aberration. By analyzing the ability of this method to restore static wavefront distortion, the feasibility and restoring ability of the CNN based wavefront restoration are verified.