A general purpose image restoration method with deep neural network and active learning

The blurred range of astronomical image data we observe is usually uncertain, Due to the complex space environment, random noise, unpredictable atmospheric turbulence and other external factors. We usually use ground-based large aperture optical telescopes to observe astronomical images, which are mainly affected by atmospheric turbulence. Therefore, the restoration of astronomical images under the influence of arbitrary atmospheric turbulence is of great significance for the theoretical development and technological progress of astronomy. In this paper, a novel astronomical image restoration algorithm is proposed, which connects the deep learning based image restoration algorithm with the data generation method. The algorithm could effectively restore images within predefined blur or noise levels. We use long exposure galaxy images and short exposure Solar images to test the algorithm. We find that a well trained algorithm can restore these images.