Astrophotography turbulence mitigation via generative models
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/
Cite
@article{arxiv.2506.02981,
title = {Astrophotography turbulence mitigation via generative models},
author = {Joonyeoup Kim and Yu Yuan and Xingguang Zhang and Xijun Wang and Stanley Chan},
journal= {arXiv preprint arXiv:2506.02981},
year = {2025}
}