Real-world image dehazing is a fundamental yet challenging task in low-level vision. Existing learning-based methods often suffer from significant performance degradation when applied to complex real-world hazy scenes, primarily due to limited training data and the intrinsic complexity of haze density distributions.To address these challenges, we introduce a novel Adaptive Patch Importance-aware (API) framework for generalizable real-world image dehazing. Specifically, our framework consists of an Automatic Haze Generation (AHG) module and a Density-aware Haze Removal (DHR) module. AHG provides a hybrid data augmentation strategy by generating realistic and diverse hazy images as additional high-quality training data. DHR considers hazy regions with varying haze density distributions for generalizable real-world image dehazing in an adaptive patch importance-aware manner. To alleviate the ambiguity of the dehazed image details, we further introduce a new Multi-Negative Contrastive Dehazing (MNCD) loss, which fully utilizes information from multiple negative samples across both spatial and frequency domains. Extensive experiments demonstrate that our framework achieves state-of-the-art performance across multiple real-world benchmarks, delivering strong results in both quantitative metrics and qualitative visual quality, and exhibiting robust generalization across diverse haze distributions.

翻译：真实世界图像去雾是底层视觉领域一项基础而具有挑战性的任务。现有的基于学习的方法在处理复杂真实世界雾霾场景时，常出现显著的性能下降，这主要源于训练数据的有限性以及雾霾密度分布的内在复杂性。为应对这些挑战，我们提出了一种新颖的自适应块重要性感知（API）框架，用于实现泛化性强的真实世界图像去雾。具体而言，我们的框架包含自动雾霾生成（AHG）模块和密度感知去雾（DHR）模块。AHG通过生成逼真且多样化的雾化图像作为额外的高质量训练数据，提供了一种混合数据增强策略。DHR则以自适应块重要性感知的方式，考虑具有不同雾霾密度分布的雾化区域，以实现泛化性强的真实世界图像去雾。为减轻去雾图像细节的模糊性，我们进一步引入了一种新的多负样本对比去雾（MNCD）损失函数，该函数充分利用了空间域和频域中多个负样本的信息。大量实验表明，我们的框架在多个真实世界基准测试中取得了最先进的性能，在定量指标和定性视觉质量方面均提供了优异的结果，并在不同雾霾分布中展现出强大的泛化能力。