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

• URL: http://arxiv.org/abs/2508.00406v1
• Date: Fri, 01 Aug 2025 08:06:41 GMT
• Title: PMR: Physical Model-Driven Multi-Stage Restoration of Turbulent Dynamic Videos
• Authors: Tao Wu, Jingyuan Ye, Ying Fu,
• Abstract summary: 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.<n>We also propose a Physical Model-Driven Multi-Stage Video Restoration ($PMR$) framework that consists of three stages: textbfde-tilting for geometric stabilization, textbfmotion segmentation enhancement, and textbfde-blurring for quality restoration.<n>$PMR$ employs lightweight backbones and stage-wise joint training to ensure both efficiency and high restoration quality.
• Score: 9.48544376032391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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