3 papers
cs.CV2025
DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection
Paul Hill, Zhiming Liu, Alin Achim +2
Atmospheric Turbulence (AT) degrades the clarity and accuracy of surveillance imagery, posing challenges not only for visualization quality but also for object classification and s…
cs.CV2025
JDATT: A Joint Distillation Framework for Atmospheric Turbulence Mitigation and Target Detection
Zhiming Liu, Paul Hill, Nantheera Anantrasirichai
Atmospheric turbulence (AT) introduces severe degradations, such as rippling, blur, and intensity fluctuations, that hinder both image quality and downstream vision tasks like targ…
cs.CV2025
MAMAT: 3D Mamba-Based Atmospheric Turbulence Removal and its Object Detection Capability
Paul Hill, Zhiming Liu, Nantheera Anantrasirichai
Restoration and enhancement are essential for improving the quality of videos captured under atmospheric turbulence conditions, aiding visualization, object detection, classificati…