Wavefront Aberrations Recognition Study Based on Multi-Channel Spatial Filter Matched with Basis Zernike Functions and Convolutional Neural Network with Xception Architecture

Abstract

The possibility of recognizing wave aberrations using a convolutional neural network with the Xception architecture is investigated based on intensity patterns at the output of a Fourier correlator with a multichannel spatial filter matched with Zernike basis functions. A dataset was calculated for training a neural network. In this dataset the intensity distribution at the correlator output was modeled for each of the first eight aberration types and their superpositions. Based on network training in 80 epochs, it was found that for the validation sample, the mean absolute error in recognizing aberrations does not exceed 0.003.