论文标题
数据 - 带有选项的驱动图像恢复 - 大小天文图像数据集的驱动学习
Data--driven Image Restoration with Option--driven Learning for Big and Small Astronomical Image Datasets
论文作者
论文摘要
图像恢复方法通常用于提高天文图像的质量。近年来，深度神经网络的发展以及天文图像数量的增加引起了许多数据 - 驱动的图像恢复方法。但是，这些方法中的大多数属于监督的学习算法，这些算法需要从真实观察结果或模拟数据作为训练集中配对的图像。对于某些应用，很难从真实观察结果中获得足够的配对图像，并且模拟图像与实际观察到的图像完全不同。在本文中，我们提出了一个基于具有选项的生成对抗网络的新数据 - 驱动的对抗网络。我们的方法使用几个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。对于具有可变观察条件的天空调查，无论参考图像的数量如何，我们的方法都可以获得非常稳定的图像恢复结果。
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.
