In UAV applications, haze significantly obscures distant details and weaken structural information, hindering the recovery of details. Current UAV scenarios still face two key challenges: (i) paired hazy/clean images from the real world are unobtainable, while the classical atmospheric scattering model is inadequate for modeling the spatially non-uniform haze in UAV imagery; (ii) existing dehazing methods struggle to remove the heavy haze accumulated in the upper regions of UAV images. To address these issues, we first propose a UAV Atmospheric Scattering Model (UASM), which explicitly incorporates flight altitude, viewing pitch, and extinction to characterize the non-uniform haze distribution in UAV imaging. Based on UASM, we develop a physics-driven dehazing framework, termed Geometry-aware Proximal Deep Unfolding Network (GP-DUN). Specifically, GP-DUN consists of three key modules: a Latent Geometry Estimator (LGE) that infers transmittance consistent with UAV imaging geometry, a Geometry-aware Gradient Descent Module (GeoGDM) that embeds UASM into the data-fidelity term and performs physics-consistent closed-form updates, and an Pooling-Expert Proximal Mapping Module (PE-PMM) that learns an implicit prior to restore textures and structures beyond the capability of explicit physical modeling. In addition, we further construct UASM-HazeSet, which provides controllable paired synthetic data together with 2,285 real UAV haze images for testing. Extensive experiments show that GP-DUN consistently outperforms existing methods on both UASM-HazeSet and real UAV haze benchmarks.

翻译：在无人机应用中，雾霾会显著模糊远处细节并削弱结构信息，阻碍细节恢复。当前无人机场景仍面临两大关键挑战：（i）真实世界中成对的雾霾/清晰图像难以获取，而经典大气散射模型无法有效建模无人机图像中空间非均匀的雾霾分布；（ii）现有去雾方法难以去除无人机图像上区域积累的浓雾。为解决这些问题，我们首先提出无人机大气散射模型（UASM），该模型显式引入飞行高度、俯仰视角和消光系数，以表征无人机成像中非均匀雾霾分布。基于UASM，我们开发了物理驱动的去雾框架——几何感知近端深度展开网络（GP-DUN）。具体而言，GP-DUN包含三个关键模块：潜在几何估计器（LGE）用于推断与无人机成像几何一致的透射率；几何感知梯度下降模块（GeoGDM）将UASM嵌入数据保真项并执行物理一致性闭式更新；池化专家近端映射模块（PE-PMM）学习隐式先验以恢复超出显式物理建模能力的纹理与结构。此外，我们进一步构建了UASM-HazeSet数据集，提供可控制的配对合成数据及2,285张真实无人机雾霾图像用于测试。大量实验表明，GP-DUN在UASM-HazeSet和真实无人机雾霾基准数据集上均持续优于现有方法。