A Transformer-Based Motion Deblurring Network for UAV Images
Rui Li, Xiaowei Zhao · 2024
When performing surveying and mapping missions using drones, motion blur is an unavoidable issue induced by several factors such as vibration, turbulence and wind during operation. Such blurring can significantly degrade the image quality, adversely affecting the accuracy and reliability of downstream applications. In this paper, to effectively eliminate the motion blur in the UAV-captured images, we propose the NAFormer based on the well-established Nonlinear Activation Free Network (NAFNet), which introduces Transformer-based blocks to further enhance its motion-deblurring ability to UAV images. The experiments based on the UAVid dataset demonstrate the effectiveness of the proposed framework.