Data--driven Image Restoration with Option--driven Learning for Big and
  Small Astronomical Image Datasets
  Image restoration methods are commonly used to improve the quality of
astronomical images. In recent years, developments of deep neural networks and
increments of the number of astronomical images have evoked a lot of
data--driven image restoration methods. However, most of these methods belong
to supervised learning algorithms, which require paired images either from real
observations or simulated data as training set. For some applications, it is
hard to get enough paired images from real observations and simulated images
are quite different from real observed ones. In this paper, we propose a new
data--driven image restoration method based on generative adversarial networks
with option--driven learning. Our method uses several high resolution images as
references and applies different learning strategies when the number of
reference images is different. For sky surveys with variable observation
conditions, our method can obtain very stable image restoration results,
regardless of the number of reference images.
