GSFNet: Gyro-Aided Spatial-Frequency Network for Motion Deblurring of UAV Infrared Images

Unmanned aerial vehicles (UAVs) with thermal imaging cameras are widely used for target tracking, reconnaissance, and search operations. However, rapid thermal camera rotations during field-of-view adjustments introduce significant motion blur, impairing real-time image detection and tracking. While deep learning has been a dominant approach for image deblurring, its application to infrared image motion deblurring (IRMD) remains limited owing to the lack of publicly available datasets and challenges in handling large motion blur or maintaining real-time performance. This study addresses these gaps by constructing a large-scale UAV infrared motion deblurring (U2IRD) benchmark dataset, incorporating gyroscopic steering rate information. Additionally, we propose a gyro-aided spatial frequency network (GSFNet) that uses spatial and frequency domain features for UAV IRMD. The input data converts the gimbal steering rate information into a pixel distribution intensity map as a priori information. Specifically, the designed spatial depth residual attention module captures critical spatial domain details, while the multiple frequency domain feature recovery module extracts frequency domain features for effective deblurring. Extensive evaluations on U2IRD and synthetic thermal blurred image datasets demonstrate that the proposed method achieves state-of-the-art deblurring performance. The new IRMD dataset, available at https://github.com/aurora-sea/U2IRD, is anticipated to facilitate advancements in UAV IRMD research and applications.