Data--driven Image Restoration with Option--driven Learning for Big and Small Astronomical Image Datasets
This addresses the challenge of limited paired images in astronomy, offering a more flexible solution for sky surveys with variable conditions, though it appears incremental as it builds on existing GAN-based approaches.
The paper tackles the problem of astronomical image restoration by proposing a data-driven method using generative adversarial networks with option-driven learning, which achieves stable results across varying numbers of reference images without requiring paired training data.
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.