Astrophotography turbulence mitigation via generative models

• URL: http://arxiv.org/abs/2506.02981v1
• Date: Tue, 03 Jun 2025 15:18:48 GMT
• Title: Astrophotography turbulence mitigation via generative models
• Authors: Joonyeoup Kim, Yu Yuan, Xingguang Zhang, Xijun Wang, Stanley Chan,
• Abstract summary: Most astronomical images captured by ground-based telescopes suffer from atmospheric turbulence, resulting in degraded imaging quality.<n>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.
• Score: 3.435619322951694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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