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.

中文翻译：

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学习天文图像的反卷积

天文图像让人们探索宇宙和监测空间；然而，由于涉及的距离很远，这些图像通常是使用望远镜设备收集的。设备光学特性和成像环境会导致图像质量下降，例如模糊、细节丢失，有时甚至会严重丢失物体结构和轮廓，从而限制了这些图像的应用。不幸的是，改进设备以获得更清晰的图像是昂贵的。因此，我们提出了一种成本低但效果令人兴奋的后处理结构学习方法来恢复天文图像。所提出的方法使用单个主干神经网络或其简单组合来解决一系列图像恢复问题，包括点扩散函数（PSF）估计、非盲反卷积、和盲反卷积。在模拟和真实天文图像的测试中，与其他最先进的方法相比，所提出的方法实现了显着的改进。虽然这项工作集中在天文图像上，但所提出的框架适用于广泛的领域。