用生成式AI把地面望远镜图像提升到太空望远镜水平
Amplifying the imaging power of digital sky surveys with space telescopes data and generative AI
- 基于星系形态特性，用太空望远镜图像训练生成模型
- 将63,202张地面图像增强至接近太空级清晰度
- 提供完整工具链，适合天文数据处理与生成研究者
地面数字巡天虽覆盖广、数据量大，但成像质量通常不如空间望远镜。空间望远镜成像清晰，可探测深空，但无法达到先进地面巡天的吞吐量。本文利用生成式AI，将地面望远镜拍摄的星系图像质量提升至接近空间望远镜水平。该方法基于星系形状的固有特征，使在空间望远镜图像上训练的生成模型能将弱信号转化为高细节、清晰的星系图像。此方案结合了地面巡天的高通量与空间望远镜的成像优势。方法源码、配对训练数据及63,202张增强后星系图像目录均已公开。我们还提供封装完整流程的软件工具与定制生成模型，支持生成高质量星系图像。
原文摘要 · Abstract (English)
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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