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Simplified non-locally dense network for single-image dehazing

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Abstract

Single-image dehazing is an ill-posed problem. Most previous methods focused on estimating intermediate parameters for input hazy images. In this paper, we propose a novel end-to-end Simplified Non-locally Dense Network (SNDN) which does not rely on intermediate parameters. To capture long-range dependencies, we propose a Simplified Non-local Dense Block (SNDB) which is lightweight and outperforms traditional non-local method. Our SNDB will be embedded into a densely connected encoder–decoder network. To avoid gradients vanishing problem, we propose a simple branch network which only have five convolution layers. The effectiveness of our proposed network is proved through ablation experiment. In addition, we enhanced our training set by synthesizing colored hazy images, which helps restore the original color of the hazy image. The experimental results demonstrate that our network have better performance than most of the pervious state-of-the-art methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61672228, Grant 61872241, Grant 61572316, and Grant 61370174, in part by the Shanghai Automotive Industry Science and Technology Development Foundation under Grant 1837, and in part by The Hong Kong Polytechnic University under Grant P0030419 and Grant P0030929.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

    Zhihua Chen & Zhuoliang Hu

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  3. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Ping Li

  4. Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, The University of Sydney, Sydney, Australia

    Jinman Kim

  5. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Enhua Wu

  6. Faculty of Science and Technology, University of Macau, Macau, China

    Enhua Wu

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  1. Zhihua Chen
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  2. Zhuoliang Hu
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  3. Bin Sheng
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  6. Enhua Wu
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Correspondence to Bin Sheng or Enhua Wu.

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Chen, Z., Hu, Z., Sheng, B. et al. Simplified non-locally dense network for single-image dehazing. Vis Comput 36, 2189–2200 (2020). https://doi.org/10.1007/s00371-020-01929-y

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