标题： 基于选项驱动学习的数据驱动图像修复方法适用于大型和小型天文图像数据集 显示英文标题

标题： Data--driven Image Restoration with Option--driven Learning for Big and Small Astronomical Image Datasets

摘要： 图像修复方法常用于提高天文学图像的质量。 近年来，深度神经网络的发展和天文学图像数量的增加激发了许多数据驱动的图像修复方法。 然而，大多数这些方法属于监督学习算法，需要成对的图像作为训练集，这些成对的图像要么来自真实观测，要么来自模拟数据。 对于某些应用来说，从真实观测中获取足够的成对图像很困难，而模拟图像与真实观测图像差异较大。 在本文中，我们提出了一种基于生成对抗网络的新的数据驱动图像修复方法，该方法采用选项驱动的学习。 我们的方法使用几张高分辨率图像作为参考，并在参考图像数量不同时应用不同的学习策略。 对于观测条件变化的天空调查，无论参考图像的数量如何，我们的方法都能获得非常稳定的图像修复结果。 显示英文摘要

摘要： 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.