Turbulence Aberration Restoration Based on Light Intensity Image Using GoogLeNet

Abstract

Adaptive optics (AO) is an effective method to compensate the wavefront distortion caused by atmospheric turbulence and system distortion. The accuracy and speed of aberration restoration are important factors affecting the performance of adaptive optics correction. In recent years, an AO correction method based on a convolutional neural network (CNN) has been proposed for the non-iterative extraction of light intensity image features and recovery of phase information. This method can directly predict the Zernike coefficient of the wavefront from the measured light intensity image and effectively improve the real-time correction ability of the AO system. In this paper, a turbulence aberration restoration based on two frames of a light intensity image using GoogLeNet is established. Three depth scales of GoogLeNet and different amounts of data training are tested to verify the accuracy of Zernike phase difference restoration at different turbulence intensities. The results show that the training of small data sets easily overfits the data, while the training performance of large data sets is more stable and requires a deeper network, which is conducive to improving the accuracy of turbulence aberration restoration. The restoration effect of third-order to seventh-order aberrations is significant under different turbulence intensities. With the increase in the Zernike coefficient, the error increases gradually. However, there are valley points lower than the previous growth for the 10th-, 15th-, 16th-, 21st-, 28th- and 29th-order aberrations. For higher-order aberrations, the greater the turbulence intensity, the greater the restoration error. The research content of this paper can provide a network design reference for turbulence aberration restoration based on deep learning.