利用空间望远镜数据和生成式人工智能增强数字化巡天（digital sky survey）的成像能力

本文提出了一种利用生成式人工智能提升地基望远镜（ground-based telescope）星系图像质量的方法，使其达到空间望远镜（space telescope）的细节水平。该方案结合了地基巡天观测（sky survey）的高通量优势与空基望远镜（space-based telescope）的高成像质量。研究团队同时发布了源代码、训练数据集及增强后的星系图像目录。

arXiv:2608.20666v1 Announce Type: new Abstract: While Digital sky surveys provide excellent throughput of image data and can cover a large footprint, their imaging power is normally inferior to that of space-based telescopes. Space-based telescopes, on the other hand, provide excellent imaging power and can image the deep Universe, but cannot provide the same throughput as advanced ground-based sky surveys. Here, we utilize generative AI to elevate the quality of galaxy images taken by ground-based telescopes to the level of details enabled by space telescopes. The solution is based on the nature of galaxy shapes, allowing generative AI trained on space-based images to convert weak signal into detailed and clear galaxy images. The method allows for combining the high throughput of ground-based sky surveys with the image quality of space-based telescopes. The source code for the method is available, as well as paired training data and a catalog of 63,202 galaxy images enhanced by the proposed method. We also provide a software tool that encapsulates the entire pipeline and the custom generative AI model to generate galaxy images with enhanced quality.