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

Source: [DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection](<https://www.sota2.com/research/paper/68fa79d51e209131cea7f9b0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-26T05:01:54.158Z

Authors: Paul Hill, Zhiming Liu, Alin Achim, Dave Bull, Nantheera Anantrasirichai

Published: 2025-07-06T10:12:02.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2507.04323>)

[PDF](<https://arxiv.org/pdf/2507.04323.pdf>)