Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing

• URL: http://arxiv.org/abs/2407.10226v1
• Date: Sun, 14 Jul 2024 14:47:32 GMT
• Title: Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing
• Authors: Shuaibin Fan, Minglong Xue, Aoxiang Ning, Senming Zhong,
• Abstract summary: This paper proposes a novel dual-branch collaborative unpaired dehazing model (DCM-dehaze) to address this issue. Specifically, we design a dual depthwise separable convolutional module (DDSCM) to enhance the information of deeper 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.
• Score: 1.6624384368855527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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