Astrophotography turbulence mitigation via generative models

2025-06-03

AI Summary

To address atmospheric turbulence-induced degradation in single-frame ground-based astronomical imaging, this paper proposes AstroDiff—the first diffusion-based generative method for single-frame turbulence suppression. Unlike conventional multi-frame approaches (e.g., lucky imaging) that rely on frame selection and manual intervention, AstroDiff employs end-to-end learning of the inverse turbulence degradation process. It jointly models high-fidelity generative priors and image restoration capability, incorporating astronomy-specific noise modeling. Under severe turbulence conditions, AstroDiff preserves celestial structural integrity and visual realism. Experiments demonstrate that AstroDiff consistently outperforms state-of-the-art methods across quantitative metrics—including PSNR and SSIM—as well as perceptual quality. Moreover, it enables real-time, high-fidelity single-frame reconstruction, overcoming the efficiency and applicability limitations inherent to multi-frame techniques.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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/

Problem

• Mitigating atmospheric turbulence in astronomical images
• Overcoming limitations of multi-frame strategies like lucky imaging
• Enhancing perceptual quality and structural fidelity in severe turbulence

Innovation

• Generative models for turbulence mitigation
• Diffusion models enhance restoration quality
• Outperforms learning-based methods significantly