Comparison of the Efficiency of Using a Convolutional Neural Network for Analyzing Wave Aberrations Based on the Diffraction Pattern on Linear and Binary Axicons

The comparative efficiency of wave aberration recognition using a convolutional neural network (CNN) with the VGG architecture based on diffraction patterns on linear and binary axicons is investigated. A dataset of 90 000 images was calculated for training the CNN, in which diffraction patterns on two types of axicons in the focal plane were simulated for each of the selected 8 types of aberrations. Based on CNN training in 40 epochs, it was shown that the achieved average absolute error in recognizing aberrations using a binary axicon does not exceed 0.0138, which is significantly less than for a linear axicon (0.193).