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Pacific Rim Conference on Multimedia

PCM 2017: Advances in Multimedia Information Processing – PCM 2017 pp 128–137Cite as

Single Image Dehazing Using Deep Convolution Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10735))

Abstract

Haze removal is urgently desired in multi-media system. A deep learning-based method, called dehazingCNN, is proposed to estimate an approximate clear image. The proposed learning model is different from traditional learning based method. We adopts Deep Convolution Neural Networks (CNN) to take a hazy image as the input and outputs the corresponding clear image directly. The output of the network is high quality except some block artifacts and color distortions. We can remove the color distortion in the approximate clear image via atmospheric scattering model and guided filter effectively. Experimental results on different type of images, such as synthetic and benchmark of hazy images, demonstrate that the proposed method is comparative to and even better than many complex state-of-the-art methods.

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Acknowledgement

National Science Foundation of China (Grant No. 61472289) and National Key Research and Development Project of China (Grant No. 2016YFC0106305).

Author information

Authors and Affiliations

  1. State Key Laboratory of Software Engineering, School of Computer Science, Wuhan University, Wuhan, China

    Shengdong Zhang & Fazhi He

  2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

    Jian Yao

Authors
  1. Shengdong Zhang
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  2. Fazhi He
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  3. Jian Yao
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Corresponding author

Correspondence to Fazhi He .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Bing Zeng

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

  3. University of Ottawa, Ottawa, Ontario, Canada

    Abdulmotaleb El Saddik

  4. University of Electronic Science and Technology of China, Chengdu, China

    Hongliang Li

  5. Chinese Academy of Sciences, Beijing, China

    Shuqiang Jiang

  6. Harbin Institute of Technology, Harbin, China

    Xiaopeng Fan

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Cite this paper

Zhang, S., He, F., Yao, J. (2018). Single Image Dehazing Using Deep Convolution Neural Networks. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-77380-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77379-7

  • Online ISBN: 978-3-319-77380-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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