Learning deconvolutions for astronomical images. Issue 1 (10th April 2021) - Long, Ma; Soubo, Yang; Cong, Shu; Weiping, Ni; Tong, Liu
ABSTRACT: Astronomical images allow people to explore the Universe and monitor space; however, due to the long distances involved, such images are generally collected using telescopic equipment. The equipment optical characteristics and the imaging environment cause image degradation, such as blurring, lost details, and sometimes serious losses of object structures and contours, thus limiting the applications of these images. Unfortunately, improving the equipment to acquire much sharper images is expensive. Therefore, we propose a post-processing structure learning method to restore astronomical images that is low in cost but has exciting effects. The proposed method uses single backbone neural networks or their simple combinations to solve a series of image restoration problems, including point spread function (PSF) estimation, non-blind deconvolution, and blind deconvolution. In tests on simulated and real astronomical images, the proposed method achieves dramatic improvements compared to other state-of-the-art methods. Although this work concentrates on astronomical images, the proposed framework is applicable to a wide range of fields.