Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real haze distributions and the scarcity of paired real-world data. To address these challenges, we first introduce a cooperative unfolding network that jointly models atmospheric scattering and image scenes, effectively integrating physical knowledge into deep networks to restore haze-contaminated details. Additionally, we propose the first RID-oriented iterative mean-teacher framework, termed the Coherence-based Label Generator, to generate high-quality pseudo labels for network training. Specifically, we provide an optimal label pool to store the best pseudo-labels during network training, leveraging both global and local coherence to select high-quality candidates and assign weights to prioritize haze-free regions. We verify the effectiveness of our method, with experiments demonstrating that it achieves state-of-the-art performance on RID tasks. Code will be available at \url{https://github.com/cnyvfang/CORUN-Colabator}.

翻译：真实世界图像去雾（RID）旨在缓解真实场景中雾霾导致的图像退化。由于准确建模真实雾霾分布的复杂性以及配对真实世界数据的稀缺性，该任务仍具挑战性。为应对这些挑战，我们首先提出一种协同展开网络，该网络联合建模大气散射与图像场景，有效将物理知识融入深度网络以恢复雾霾污染的细节。此外，我们提出了首个面向RID的迭代均值教师框架，称为基于相干性的标签生成器，用于为网络训练生成高质量伪标签。具体而言，我们构建了一个最优标签池以存储网络训练期间的最佳伪标签，利用全局与局部相干性筛选高质量候选标签并分配权重以优先处理无雾区域。我们验证了所提方法的有效性，实验表明其在RID任务上达到了最先进的性能。代码将发布于 \url{https://github.com/cnyvfang/CORUN-Colabator}。