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DRDDN: dense residual and dilated dehazing network

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Abstract

Recently, deep convolutional neural networks (CNNs) have made great achievements in image restoration. However, there exists a large space to improve the performance of CNN-based dehazing model. In this paper, we design a fully end-to-end dehazing network, which can be called as dense residual and dilated dehazing network (DRDDN), for single image dehazing. In detail, a dilated densely connected block is designed to fully exploit multi-scale features through an adaptive learning process. The receptive field of the network is enlarged by dilation convolution without losing spatial information. Furthermore, we use deep residual to propagate the low-level features to high-level layers. Therefore, our model can fully exploit both low-level and high-level features for dehazing. Experiments on benchmark datasets and real-world hazy images show that the proposed DRDDN achieves favorable performance against the state-of-the-art methods.

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Acknowledgements

We thank anonymous reviewers very much for their suggestive comments that improve the quality of this paper. Shengdong Zhang and Fazhi He were partially supported by the NSFC (Nos. 61472289, 41571436). Shengdong Zhang were partially supported by Public Welfare Technology Application Research Project of Zhejiang Province (No. LGG22F010004).

Funding

Funding was provided by National Key Research and Develop Program of China (Grant No. 2017YFB0503004).

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Authors and Affiliations

  1. Computer Science Engineering Department, Shaoxing University, Shaoxing, 312000, China

    Shengdong Zhang

  2. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Fazhi He

  3. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, 300300, China

    Jiaoting Zhang

  4. School of Computer Science, Yangtze University, Jingzhou, 434023, China

    Neng Hou

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  1. Shengdong Zhang
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Correspondence to Fazhi He.

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Zhang, S., Zhang, J., He, F. et al. DRDDN: dense residual and dilated dehazing network. Vis Comput 39, 953–969 (2023). https://doi.org/10.1007/s00371-021-02377-y

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