用扩散模型修复地基望远镜受大气湍流影响的天文照片
Astrophotography turbulence mitigation via generative models
- 基于扩散模型，利用生成先验和恢复能力去消除湍流影响
- 在严重湍流下仍保持更高视觉质量和结构保真度
- 适合需要高质量天文图像的科研人员和图像处理工程师
摄影是现代天文学与空间研究的核心手段。然而，地面望远镜拍摄的大多数天文图像会受到大气湍流影响，导致成像质量下降。尽管多帧策略（如幸运成像）可部分缓解此问题，但需大量数据采集和复杂的手动处理。本文提出 AstroDiff，一种基于生成模型的图像恢复方法，充分利用扩散模型的高质量生成先验与修复能力，有效抑制大气湍流带来的退化。大量实验表明，AstroDiff 在天文图像湍流抑制方面优于现有最先进的学习型方法，在严重湍流条件下仍能提供更高的感知质量与更好的结构保真度。代码与附加结果见 https://web-six-kappa-66.vercel.app/
原文摘要 · Abstract (English)
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/
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