Wavefront Restoration Method Based on Light Intensity Image Deep Learning

Wavefront restoration based on deep learning is to obtain Zernike coefficients of wavcfront 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 wavcfront 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 wavcfront restoration model is established based on the 35 -order Zernike-atmospheric turbulence aberration. By analyzing the ability of this method to restore static wavcfront distortion, the feasibility and restoring ability of the CNN based wavcfront restoration arc verified.