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arXiv · 2507.04323

DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection

Paul Hill·Zhiming Liu·Alin Achim·Dave Bull·Nantheera Anantrasirichai
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Abstract

Atmospheric Turbulence (AT) degrades the clarity and accuracy of surveillance imagery, posing challenges not only for visualization quality but also for object classification and scene tracking. Deep learning-based methods have been proposed to improve visual quality, but spatio-temporal distortions remain a significant issue. Although deep learning-based object detection performs well under normal conditions, it struggles to operate effectively on sequences distorted by atmospheric turbulence. In this paper, we propose a novel framework that learns to compensate for distorted features while simultaneously improving visualization and object detection. This end-to-end training strategy leverages and exchanges knowledge of low-level distorted features in the AT mitigator with semantic features extracted in the object detector. Specifically, in the AT mitigator a 3D Mamba-based structure is used to handle the spatio-temporal displacements and blurring caused by turbulence. Optimization is achieved through back-propagation in both the AT mitigator and object detector. Our proposed DMAT outperforms state-of-the-art AT mitigation and object detection systems up to a 15% improvement on datasets corrupted by generated turbulence.

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Paul Hill, Zhiming Liu, Alin Achim, Dave Bull, Nantheera Anantrasirichai. 2025-11-22. DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection. https://arxiv.org/abs/2507.04323

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