标题： PMR：物理模型驱动的湍流动态视频多阶段恢复 显示英文标题

标题： PMR: Physical Model-Driven Multi-Stage Restoration of Turbulent Dynamic Videos

摘要： 由大气湍流引起的几何失真和模糊会降低远距离动态场景视频的质量。 现有方法在恢复边缘细节和消除混合失真方面存在困难，尤其是在强湍流和复杂动态条件下。 为了解决这些挑战，我们引入了一个动态效率指数（$DEI$），该指数结合了湍流强度、光流和动态区域的比例，以在不同湍流条件下准确量化视频动态强度，并提供一个高动态湍流训练数据集。 此外，我们提出了一种物理模型驱动的多阶段视频恢复框架（$PMR$），该框架包括三个阶段：\textbf{去倾斜}用于几何稳定，\textbf{运动分割增强}用于动态区域细化，\textbf{去模糊}用于质量恢复。 $PMR$采用轻量级主干网络和分阶段联合训练，以确保效率和高质量的恢复。 实验结果表明，所提出的方法能有效抑制运动拖尾伪影，恢复边缘细节，并表现出强大的泛化能力，特别是在具有高湍流和复杂动态的真实场景中。 我们将公开代码和数据集。 显示英文摘要

摘要： Geometric distortions and blurring caused by atmospheric turbulence degrade the quality of long-range dynamic scene videos. Existing methods struggle with restoring edge details and eliminating mixed distortions, especially under conditions of strong turbulence and complex dynamics. To address these challenges, we introduce a Dynamic Efficiency Index ($DEI$), which combines turbulence intensity, optical flow, and proportions of dynamic regions to accurately quantify video dynamic intensity under varying turbulence conditions and provide a high-dynamic turbulence training dataset. Additionally, we propose a Physical Model-Driven Multi-Stage Video Restoration ($PMR$) framework that consists of three stages: \textbf{de-tilting} for geometric stabilization, \textbf{motion segmentation enhancement} for dynamic region refinement, and \textbf{de-blurring} for quality restoration. $PMR$ employs lightweight backbones and stage-wise joint training to ensure both efficiency and high restoration quality. Experimental results demonstrate that the proposed method effectively suppresses motion trailing artifacts, restores edge details and exhibits strong generalization capability, especially in real-world scenarios characterized by high-turbulence and complex dynamics. We will make the code and datasets openly available.