Aberrations estimate based on diffraction patterns using deep residual network

Abstract

Lenses are fundamental elements in many optical applications. However, various aberrations are inevitably present in lenses, which will affect the distribution of focused light intensity and optical imaging. Accurately predicting the aberrations of a lens is of great significance. Nevertheless, quantitatively measuring the aberrations of a lens, especially when multiple aberrations are present simultaneously, is a challenging task. In this paper, we propose a method based on a designed deep residual network called Y-ResNet to measure the astigmatism and coma of a lens simultaneously. The Y-ResNet was trained on the focused image pattern of a Gaussian beam passing through a lens with astigmatism and coma. The trained network can accurately predict the aberration coefficients of the lens with 0.99 specificity, 0.925 precision, 0.9382 recall, and a 0.9406 F1-score achieved on astigmatism and 0.99 specificity, 0.956 precision, 0.98 recall, and a 0.954 F1-score achieved on coma. Specifically, even if only part of the intensity distribution of the light spot is captured, the network can accurately estimate the aberrations of the lens with an accuracy of over 90% on coma and can identify astigmatism aberration features. This paper can provide a feasible method for correcting beam patterns caused by aberration based on deep learning.