Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing
<|reference_start|>Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing: Although synthetic data can alleviate acquisition challenges in image dehazing tasks, it also introduces the problem of domain bias when dealing with small-scale data. This paper proposes a novel dual-branch collaborative unpaired dehazing model (DCM-dehaze) to address this issue. The proposed method consists of two collaborative branches: dehazing and contour constraints. Specifically, we design a dual depthwise separable convolutional module (DDSCM) to enhance the information expressiveness of deeper features and the correlation to shallow features. In addition, we construct a bidirectional contour function to optimize the edge features of the image to enhance the clarity and fidelity of the image details. Furthermore, we present feature enhancers via a residual dense architecture to eliminate redundant features of the dehazing process and further alleviate the domain deviation problem. Extensive experiments on benchmark datasets show that our method reaches the state-of-the-art. This project code will be available at \url{https://github.com/Fan-pixel/DCM-dehaze.<|reference_end|>
@article{fan2024addressing,
    title={Addressing Domain Discrepancy: A Dual-branch Collaborative Model to
  Unsupervised Dehazing},
    author={Shuaibin Fan, Minglong Xue, Aoxiang Ning, Senming Zhong},
    journal={arXiv preprint arXiv:2407.10226},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10226},
    primaryClass={cs.CV}
}