Wavefront Aberrations Recognition Study Based on Multi-Channel Spatial Filter Matched with Basis Zernike Functions and Convolutional Neural Network with Xception Architecture
Alexey P. Dzyuba, Pavel A. Khorin, П. Г. Серафимович, Svetlana N. Khonina
Abstract
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.
Topics & Concepts
Zernike polynomialsComputer scienceWavefrontConvolutional neural networkPattern recognition (psychology)Artificial intelligenceFilter (signal processing)Basis (linear algebra)Network architectureFourier transformFilter bankAlgorithmComputer visionOpticsMathematicsPhysicsGeometryMathematical analysisComputer securityAdaptive optics and wavefront sensingOptical Polarization and EllipsometryOptical measurement and interference techniques