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

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...

Full description

Saved in:
Bibliographic Details
Published in:Optical memory & neural networks 2024-12, Vol.33 (Suppl 1), p.S53-S64
Main Authors: Dzyuba, A. P., Khorin, P. A., Serafimovich, P. G., Khonina, S. N.
Format: Article
Language:English
Subjects:
Complexity
Computer Science
Information Storage and Retrieval
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1060-992X
1934-7898
DOI:10.3103/S1060992X24700309