Image dehazing has witnessed significant advancements with the development of deep learning models. However, a few methods predominantly focus on single-modal RGB features, neglecting the inherent correlation between scene depth and haze distribution. Even those that jointly optimize depth estimation and image dehazing often suffer from suboptimal performance due to inadequate utilization of accurate depth information. In this paper, we present UDPNet, a general framework that leverages depth-based priors from large-scale pretrained depth estimation model DepthAnything V2 to boost existing image dehazing models. Specifically, our architecture comprises two typical components: the Depth-Guided Attention Module (DGAM) adaptively modulates features via lightweight depth-guided channel attention, and the Depth Prior Fusion Module (DPFM) enables hierarchical fusion of multi-scale depth map features by dual sliding-window multi-head cross-attention mechanism. These modules ensure both computational efficiency and effective integration of depth priors. Moreover, the intrinsic robustness of depth priors empowers the network to dynamically adapt to varying haze densities, illumination conditions, and domain gaps across synthetic and real-world data. Extensive experimental results demonstrate the effectiveness of our UDPNet, outperforming the state-of-the-art methods on popular dehazing datasets, such as 0.85 dB PSNR improvement on the SOTS dataset, 1.19 dB on the Haze4K dataset and 1.79 dB PSNR on the NHR dataset. Our proposed solution establishes a new benchmark for depth-aware dehazing across various scenarios. Pretrained models and codes will be released at our project https://github.com/Harbinzzy/UDPNet.

翻译：随着深度学习模型的发展，图像去雾领域已取得显著进展。然而，现有方法大多集中于单模态RGB特征，忽视了场景深度与雾霾分布之间的内在关联。即使是那些联合优化深度估计与图像去雾的方法，也常因未能充分利用精确深度信息而导致性能欠佳。本文提出UDPNet，一个通用框架，其利用来自大规模预训练深度估计模型DepthAnything V2的基于深度的先验知识，以增强现有图像去雾模型的性能。具体而言，我们的架构包含两个典型组件：深度引导注意力模块通过轻量级的深度引导通道注意力自适应地调制特征；深度先验融合模块则通过双滑动窗口多头交叉注意力机制实现多尺度深度图特征的分层融合。这些模块确保了计算效率与深度先验的有效整合。此外，深度先验的内在鲁棒性使网络能够动态适应不同的雾霾浓度、光照条件以及合成数据与真实数据之间的域差异。大量实验结果证明了我们UDPNet的有效性，其在主流去雾数据集上超越了现有最先进方法，例如在SOTS数据集上PSNR提升0.85 dB，在Haze4K数据集上提升1.19 dB，在NHR数据集上提升1.79 dB。我们提出的解决方案为各种场景下的深度感知去雾建立了新的基准。预训练模型和代码将在我们的项目页面https://github.com/Harbinzzy/UDPNet发布。