A frequency-aware multi-task learning framework for collaborative dehazing and semantic segmentation of oblique UAV images
Ziquan Wang, Li Li, Yongsheng Zhang, Zhipeng Jiang, Yu Ying, Zhenchao Zhang, Zimian Wei · International Journal of Image and Data Fusion · 2026
The rapid growth of the low-altitude economy has made Unmanned Aerial Vehicle (UAV) scene perception critical for applications like environmental monitoring, but fog degrades image quality – requiring simultaneous dehazing (to restore details) and semantic segmentation (for scene understanding). These tasks conflict: dehazing prioritises high-frequency details, while segmentation relies on low-frequency semantics, and traditional joint frameworks lack dedicated feature control, under-performing in foggy scenes. Conventional methods struggle with frequency issues or fog generalisation, and UAV-specific hazy segmentation datasets are scarce. Thus, we propose Dehaze-Seg, a frequency-aware multi-task framework for foggy UAV perception including: 1) the Local Fusion Module (LFM) uses Haar Wavelet to balance detail restoration and semantics; 2) the Interactive Fusion Module (IFM) corrects mid-level feature distortion; 3) the Semantic Enhance Module (SEM) uses DCT to disentangle fog and deep semantics. We also build two UAV-view hazy segmentation datasets called HazyUAVid, HazyUDD. Experiments show Dehaze-Seg outperforms state-of-the-art methods on dehazing (PSNR, SSIM) and segmentation (mIoU, F1-score) metrics, with strong generalisation to ground-view foggy scenes. Codes and datasets are available in blue at https://github.com/aresdrw/dehaze-seg.