Abstract
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.
Abstract (translated)
大气湍流引起的几何失真和模糊会降低长距离动态场景视频的质量。现有的方法在恢复边缘细节和消除混合失真方面面临挑战，尤其是在强湍流和复杂动态条件下表现不佳。为了解决这些问题，我们引入了一个动态效率指标（Dynamic Efficiency Index, $DEI$），它结合了湍流强度、光流以及动态区域的比例来准确量化不同湍流条件下的视频动态强度，并提供了高动态湍流训练数据集。此外，我们还提出了一种基于物理模型的多阶段视频恢复框架（Physical Model-Driven Multi-Stage Video Restoration, $PMR$），该框架包括三个阶段：**去倾斜**以实现几何稳定化、**运动分割增强**以优化动态区域、以及**去模糊**以提升图像质量。$PMR$ 使用轻量级骨干网络和分阶段联合训练，确保高效的同时保持高质量的恢复效果。 实验结果表明，所提出的方法能够有效抑制运动拖尾伪影，恢复边缘细节，并且在特征为高湍流和复杂动态的真实世界场景中表现出强大的泛化能力。我们将开源代码和数据集。
URL
https://arxiv.org/abs/2508.00406
