Open AccessNeural networks application to determine the types and magnitude of aberrations from the pattern of the point spread function out of the focal plane
Author(s) -
Pavel A. Khorin,
Alexey P. Dzyuba,
П. Г. Серафимович,
С. Н. Хонина
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2086/1/012148
Subject(s) - zernike polynomials , cardinal point , point spread function , plane (geometry) , optics , focal point , artificial neural network , function (biology) , intensity (physics) , magnitude (astronomy) , convolutional neural network , point (geometry) , artificial intelligence , physics , computer science , pattern recognition (psychology) , mathematics , geometry , wavefront , astrophysics , evolutionary biology , biology
Recognition of the types of aberrations corresponding to individual Zernike functions were carried out from the pattern of the intensity of the point spread function (PSF) outside the focal plane using convolutional neural networks. The PSF intensity patterns outside the focal plane are more informative in comparison with the focal plane even for small values/magnitudes of aberrations. The mean prediction errors of the neural network for each type of aberration were obtained for a set of 8 Zernike functions from a dataset of 2 thousand pictures of out-of-focal PSFs. As a result of training, for the considered types of aberrations, the obtained averaged absolute errors do not exceed 0.0053, which corresponds to an almost threefold decrease in the error in comparison with the same result for focal PSFs.