HDCFN: Haze Distribution-aware Cross-modal Fusion Network for Infrared-guided Dense Haze Removal in UAVs

Junwei Zhao, Qianchun Luo, Shiliang Zhang, Shen Gao, Jie Wu

Abstract: In UAV applications, dense haze severely obscures small ground-level objects, hindering the recovery of fine details. Existing visible-only dehazing methods struggle with such dense occlusions, while infrared imaging lacks color and fine texture information. To address these limitations, we propose the Haze Distribution-aware Cross-modal Fusion Network (HDCFN). HDCFN features two key components: (i) an infrared-guided multiscale feature enhancement framework that integrates haze-resistant structural cues from infrared modality with visible features across coarse to fine, improving the recovery of small objects, and (ii) a haze distribution-aware cross-modal fusion module that adaptively prioritizes relevant information from each modality according to haze density. This framework effectively combines the complementary strengths of visible and infrared imaging for dense haze removal. Extensive experiments on multiple public datasets show that HDCFN outperforms state-of-the-art dehazing and fusion methods, yielding higher-quality and more detailed images.