MAMAT：基于3D Mamba的大气湍流消除及其目标检测能力
MAMAT: 3D Mamba-Based Atmospheric Turbulence Removal and its Object Detection Capability
AI总结：
针对大气湍流导致的监控视频质量下降问题，提出基于3D Mamba的双模块MAMAT方法，通过可变形3D卷积配准和细节增强，在视觉质量和目标检测精度上均优于现有学习方法。
摘要：
图像恢复与增强对于提升大气湍流条件下拍摄的视频质量至关重要，可辅助监控系统中的可视化、目标检测、分类与跟踪任务。本文提出一种基于Mamba的新方法——基于3D Mamba的大气湍流消除（MAMAT），采用双模块策略缓解此类失真。第一个模块使用可变形3D卷积进行非刚性配准，以最小化空间偏移；第二个模块增强对比度与细节。借助3D Mamba架构的先进能力，实验结果表明MAMAT优于现有最先进的基于学习的方法，视觉质量提升最高达3%，目标检测性能提升15%。该方法不仅增强了可视化效果，还显著提高了目标检测精度，填补了视觉恢复与监控应用有效性之间的空白。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
