标题： 基于生成模型的天文摄影湍流抑制 显示英文标题

标题： Astrophotography turbulence mitigation via generative models

摘要： 摄影是现代天文和空间研究的基石。然而，大多数由地面望远镜捕获的天文图像都会受到大气湍流的影响，导致成像质量下降。尽管多帧策略（如幸运成像）可以缓解部分影响，但它们涉及大量的数据采集和复杂的手动处理。 在这篇论文中，我们提出了AstroDiff，这是一种生成式修复方法，利用扩散模型的高质量生成先验和修复能力来减轻大气湍流的影响。广泛的实验表明，AstroDiff在减轻天文图像湍流方面优于现有的最先进的基于学习的方法，在严重湍流条件下提供了更高的感知质量和更好的结构保真度。 我们的代码和附加结果可以在https://web-six-kappa-66.vercel.app/获得。 显示英文摘要

摘要： Photography is the cornerstone of modern astronomical and space research. However, most astronomical images captured by ground-based telescopes suffer from atmospheric turbulence, resulting in degraded imaging quality. While multi-frame strategies like lucky imaging can mitigate some effects, they involve intensive data acquisition and complex manual processing. In this paper, we propose AstroDiff, a generative restoration method that leverages both the high-quality generative priors and restoration capabilities of diffusion models to mitigate atmospheric turbulence. Extensive experiments demonstrate that AstroDiff outperforms existing state-of-the-art learning-based methods in astronomical image turbulence mitigation, providing higher perceptual quality and better structural fidelity under severe turbulence conditions. Our code and additional results are available at https://web-six-kappa-66.vercel.app/