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International Conference on Knowledge Science, Engineering and Management

KSEM 2016: Knowledge Science, Engineering and Management pp 314–324Cite as

Single Image Dehazing Using Hölder Coefficient

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9983))

Abstract

Restoring the true scene appearance from hazy image is a challenging task and one of a most necessary part in image processing system. As we know, the clear-day image must have higher contrast compared to the hazy image. Our main idea is that the hazy image is enhanced based on this observation to achieve dehazing objective. Firstly, we use a new metric, simple but powerful Hölder coefficient, to estimate the hazy density roughly. In order to make the estimation density map more reasonable, we apply proposed energy function to refine it. Based on the refined map, we propose a new method to estimate the atmosphere light. Secondly, three new terms, which are used to enhance image, are modeled into energy function. Solving this energy function, transmission map can be obtained. Finally, we get haze-free image by using the transmission map. Experiment results demonstrate that our algorithm has similar or better performance compared to the state-of-art algorithms.

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Acknowledgement

This work is supported by the National Science Foundation of China (No. 61273298), and Science and Technology Commission of Shanghai Municipality under research grant No. 14DZ2260800.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241, China

    Dehao Shang, Tingting Wang & Faming Fang

Authors
  1. Dehao Shang
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  2. Tingting Wang
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  3. Faming Fang
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Corresponding author

Correspondence to Faming Fang .

Editor information

Editors and Affiliations

  1. University of Passau, Passau, Germany

    Franz Lehner

  2. University of Passau , Passau, Germany

    Nora Fteimi

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Shang, D., Wang, T., Fang, F. (2016). Single Image Dehazing Using Hölder Coefficient. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_25

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  • DOI: https://doi.org/10.1007/978-3-319-47650-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47649-0

  • Online ISBN: 978-3-319-47650-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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