Real-world Image Dehazing with Coherence-based Label Generator and Cooperative Unfolding Network
<|reference_start|>Real-world Image Dehazing with Coherence-based Label Generator and Cooperative Unfolding Network: 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}.<|reference_end|>
@article{fang2024real-world,
    title={Real-world Image Dehazing with Coherence-based Label Generator and
  Cooperative Unfolding Network},
    author={Chengyu Fang, Chunming He, Fengyang Xiao, Yulun Zhang, Longxiang Tang,
  Yuelin Zhang, Kai Li, Xiu Li},
    journal={arXiv preprint arXiv:2406.07966},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07966},
    primaryClass={cs.CV}
}