Blind restoration of astronomical image based on deep attention generative adversarial neural network

Abstract

The imaging quality of astronomical targets observed by ground-based telescopes is affected by atmospheric turbulence and the image resolution is seriously reduced. A deep attention generative adversarial network is proposed to restore the astronomical image and to learn the end-to-end imaging law between the blurred image and the ground truth image from image dataset directly. The attention mechanism module is designed to improve the performance of the network. Based on the conventional theory of atmospheric imaging of telescopes and combining optical system parameters, a series of astronomical images are simulated to establish a dataset for training networks. The proposed method is verified by simulated test image and real astronomical image. The experimental results show that the proposed method can effectively eliminate the influence of atmospheric turbulence and improve the resolution of astronomical images. We demonstrate the possible and good prospects for future applications of deep learning to high-resolution imaging of astronomical images.