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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…
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…
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…
Deep Learning Techniques for Atmospheric Turbulence Removal: A Review
Paul Hill, Nantheera Anantrasirichai, Alin Achim +1
The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approach…
Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors
P. Hill, N. Anantrasirichai, A. Achim +1
Atmospheric turbulence poses a challenge for the interpretation and visual perception of visual imagery due to its distortion effects. Model-based approaches have been used to addr…