用3D Mamba模型去模糊湍流视频，提升画质和目标检测准确率。
MAMAT: 3D Mamba-Based Atmospheric Turbulence Removal and its Object Detection Capability
- 采用双模块设计：3D可变形卷积对齐形变，增强对比度与细节。
- 相比顶尖方法，视觉质量提升3%，目标检测准确率提高15%。
- 适合需要高精度视觉恢复与检测的监控系统应用。
在大气湍流条件下拍摄的视频需进行复原与增强，以提升可视化效果，并支持监控系统中的目标检测、分类与追踪。本文提出一种基于3D Mamba架构的新方法——MAMAT，采用双模块策略缓解此类畸变。第一模块利用可变形3D卷积进行非刚性配准，减少空间位移；第二模块增强对比度与细节。实验表明，相较于现有最先进学习方法，MAMAT在视觉质量上最高提升3%，目标检测准确率提升15%。该方法不仅改善视觉效果，更显著提升监控应用中目标检测的有效性，弥合了图像复原与实际应用之间的差距。
原文摘要 · Abstract (English)
Restoration and enhancement are essential for improving the quality of videos captured under atmospheric turbulence conditions, aiding visualization, object detection, classification, and tracking in surveillance systems. In this paper, we introduce a novel Mamba-based method, the 3D Mamba-Based Atmospheric Turbulence Removal (MAMAT), which employs a dual-module strategy to mitigate these distortions. The first module utilizes deformable 3D convolutions for non-rigid registration to minimize spatial shifts, while the second module enhances contrast and detail. Leveraging the advanced capabilities of the 3D Mamba architecture, experimental results demonstrate that MAMAT outperforms state-of-the-art learning-based methods, achieving up to a 3\% improvement in visual quality and a 15\% boost in object detection. It not only enhances visualization but also significantly improves object detection accuracy, bridging the gap between visual restoration and the effectiveness of surveillance applications.
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