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
https://github.com/cnyvfang/CORUN-Colabator.

真实世界图像去雾任务中，我们首先引入了一个协作展开网络来恢复受雾影响的细节，同时提出了第一个面向去雾任务的迭代式均值教师框架，该框架可以生成高质量的伪标签用于网络训练，并通过优化标签池来挑选高质量的候选样本并分配权重以优先选择不受雾影响的区域，实验证明我们的方法在去雾任务上达到了最先进的性能。