Abstract:

Lucky imaging constitutes a pivotal approach for restoring turbulence-degraded imagery. Recent advances have explored method optimizations across diverse application scenarios. However， existing reviews published years ago do not cover breakthroughs from the past decade. This study conducts a thorough investigation of cutting-edge algorithms， first introducing classical methodologies and outlining persistent challenges. We then elaborate on the developments and applications through three dimensions： real-time implementation， multi-target adaptability， and integration with complementary image processing techniques. A dedicated turbulence dataset is released alongside systematic analysis of benchmark datasets， evaluation metrics， and comparative performance of leading methods. Scenario-specific applicability and inherent limitations are analyzed， culminating in six future trajectories： GPU-edge heterogeneous computing， dynamic turbulence modeling with non-stationary compensation， data-driven end-to-end fusion， multi-modal cross-scale restoration， event-camera-based dynamic imaging， and standardized evaluation frameworks with open-source ecosystems.

Key words: lucky imaging, image restoration, turbulence effect, astronomical image processing, real-time imaging, multi-scale restoration

摘要：

幸运成像是湍流退化图像复原方法中的一个重要分支。近年来，研究人员针对不同应用场景和不同优化方向对基于幸运成像的复原方法进行了探索。但有关综述发表年代较早，未能涵盖近十年来该技术的突破性进展，故对幸运成像前沿算法展开全面深入的调查。介绍了幸运成像的经典方法，梳理了面临的挑战。从实时幸运成像、幸运成像应用于不同目标、幸运成像与其他图像处理方法相结合这3个方面详细阐述了幸运成像的发展与应用。公开了一个自制湍流退化图像数据集并系统总结了其他数据集和复原评估指标，以及对比了基于幸运成像的代表性方法的性能，讨论了幸运成像的场景适应性和局限性。从基于GPU边缘计算的实时异构架构设计、动态湍流建模与非平稳性补偿、数据驱动的端到端融合方法、多模态数据协同与跨尺度复原、基于事件相机的动态湍流成像和标准化评估体系与开源生态构建这6个方面指出未来可能的发展趋势。

关键词: 幸运成像, 图像复原, 湍流效应, 天文图像处理, 实时成像, 多尺度复原