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Hazy Removal via Graph Convolutional with Attention Network

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Abstract

Most deep learning based single image dehazing methods use convolutional neural networks (CNN) to extract features, however CNN can only capture local features. To address the limitations of CNN, We propose a basic module that combines CNN and graph convolutional network (GCN) to capture both local and non-local features. The basic module consist of a CNN with triple attention modules (CAM) and a dual GCN module (DGM). CAM that combines the channel attention, spatial attention and pixel attention is designed to earn more weight from important local features. DGM combines spatial coherence computing and channel correlation computing to extract non-local information. The architecture of the network is similar to U-Net, and skip connections used in the symmetrical network can pass the image details from shallow layers to deep layers. Experimental results in several datasets indicate that the proposed method outperforms the state-of-the-arts both quantitatively and qualitatively.

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Data Availability

All data generated or analyzed during this study are included in this published article [35,36,37,38].

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Acknowledgements

This work is supported by Jiangsu industry research project BY2020552.

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Authors and Affiliations

  1. School of Information Science and Technology, Nantong University, Nantong, Jiangsu, China

    Bin Hu, Zhuangzhuang Yue, Mingcen Gu, Yan Zhang, Zhen Xu & Jinhang Li

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  1. Bin Hu
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  2. Zhuangzhuang Yue
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  3. Mingcen Gu
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  4. Yan Zhang
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  5. Zhen Xu
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  6. Jinhang Li
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Correspondence to Bin Hu.

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Hu, B., Yue, Z., Gu, M. et al. Hazy Removal via Graph Convolutional with Attention Network. J Sign Process Syst 95, 517–527 (2023). https://doi.org/10.1007/s11265-023-01863-x

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  • DOI: https://doi.org/10.1007/s11265-023-01863-x

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