发表机构
Kansas State University（堪萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于生成式AI的方法，结合空间望远镜数据，将地面巡天的星系图像质量提升至空间望远镜水平，公开了相关代码、数据与软件工具，实现了地面巡天高通量与空间望远镜高成像质量的融合。
AI 中文摘要
数字天空巡天虽然能提供出色的图像数据通量并覆盖大面积天区，但其成像能力通常不及空间望远镜；而空间望远镜虽具备出色的成像能力且可观测宇宙深处，却无法达到先进地面巡天的通量水平。本研究利用生成式AI将地面望远镜拍摄的星系图像质量提升至空间望远镜所能达到的细节水平，该方法基于星系形状的特性，通过在空间望远镜图像上训练的生成式AI将弱信号转换为细节清晰的星系图像，实现了将地面巡天的高通量与空间望远镜的图像质量相结合。该方法的源代码、配对训练数据以及经该方法增强的63202幅星系图像目录均已公开，同时提供了封装完整流程和定制生成式AI模型的软件工具，用于生成质量增强的星系图像。
英文摘要
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.
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