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Detail-Enhancement for Dehazing Method Using Guided Image Filter and Laplacian Pyramid

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Advances in Multimedia Information Processing – PCM 2017 (PCM 2017)

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Abstract

Using dark channel prior (DCP) with guided image filter (GIF) is one of the most attention haze removal methods in recent years. However, this method may lead to blurring phenomenon in the dehazed image. This work focus on address this issue by constructing a differential model to look for the causes of the blurry vision. Inspired by this model, we proposed a detail-enhancement method using Laplacian pyramid technology. One of the advantages of this method is that, it can simultaneously achieve dehazing and detail-enhancing while without additional computational complexity. The experimental results show that the proposed method can effectively enhance the edge of the dehazed image.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (NSFC) under Grants 61232016, 61572461, 11790305, CAS ‘100-Talents’ (Dr. Xu Long).

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Authors and Affiliations

  1. Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100101, China

    Dong Zhao & Long Xu

Authors
  1. Dong Zhao
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  2. Long Xu
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Corresponding author

Correspondence to Long Xu .

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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Zhao, D., Xu, L. (2018). Detail-Enhancement for Dehazing Method Using Guided Image Filter and Laplacian Pyramid. 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_53

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  • DOI: https://doi.org/10.1007/978-3-319-77380-3_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77379-7

  • Online ISBN: 978-3-319-77380-3

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