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Modified Image Dehazing Method Based on Dark Channel Prior

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Advances in Smart Vehicular Technology, Transportation, Communication and Applications (VTCA 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 128))

Abstract

Image dehazing is an important research topic in the field of image processing and computer vision. Image dehazing aims to remove haze in images and make image scenes clearer. Image dehazing based on dark channel prior is a currently popular type of methods. However, image dehazing results obtained by existing methods based on dark channel prior usually have color distortion and low brightness causing partial image details invisible. To alleviate this issue, we presented a modified method based on dark channel prior. First, the proposed method estimates the value of atmospheric light by using a quadtree algorithm, and uses the dark channel prior to pixelwisely estimate and optimize medium transmission. Second, the proposed method uses a classic atmospheric scattering model to generate an initial image dehazing result, and transforms the result from RGB (Red, Green, and Blue) color space to HSV (Hue, Saturation, and Value) color space. Finally, the proposed method conducts the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm on the V component of the initial result, and maps the result into the RGB space to obtain final image dehazing result. Experimental results showed that the proposed method effectively alleviated color distortion and made image scenes clearer in image dehazing results.

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Acknowledgment

This work is partially supported by National Natural Science Foundation of China (61772254), National Nature Science Foundation of China (Grant number: 61871204), Fujian Provincial Leading Project (2017H0030 and 2018H0028), Key Project of College Youth Natural Science Foundation of Fujian Province (JZ160467), Fuzhou Science and Technology Project (2018-S-123 and 2016-S-116), New Century Excellent Talents in Fujian Province University (NCETFJ), Project of Minjiang University (MYK17021), and Major Project of Sichuan Province Key Laboratory of Digital Media Art (17DMAKL01).

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Junpeng Hu

  2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China

    Junpeng Hu, Xinrong Cao, Xinwei Chen, Zuoyong Li & Fuquan Zhang

Authors
  1. Junpeng Hu
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  2. Xinrong Cao
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  3. Xinwei Chen
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  4. Zuoyong Li
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  5. Fuquan Zhang
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Corresponding author

Correspondence to Zuoyong Li .

Editor information

Editors and Affiliations

  1. College of Physics and Energy, Fujian Normal University, Fuzhou, Fujian, China

    Yong Zhao

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Tsu-Yang Wu

  3. School of Transportation, Fujian University of Technology, Fuzhou, Fujian, China

    Tang-Hsien Chang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  5. University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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Hu, J., Cao, X., Chen, X., Li, Z., Zhang, F. (2019). Modified Image Dehazing Method Based on Dark Channel Prior. In: Zhao, Y., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-04585-2_19

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