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The Visual Computer

, Volume 32, Issue 6–8, pp 911–920 | Cite as

Texture filtering based physically plausible image dehazing

  • Chunxiao LiuEmail author
  • Jinwei Zhao
  • Yiyun Shen
  • Yanggang Zhou
  • Xun Wang
  • Yi Ouyang
Original Article

Abstract

To address the issues of false candidate atmospheric light, halo effects and color distortion in sky regions, a physically plausible single image dehazing algorithm is proposed based on texture filtering. First, gamma correction based preprocessing is applied to the luminance channel of the haze image, which improves the luminance and contrast of the haze image simultaneously. Second, a support vector machine based classifier is trained and utilized to reject the false candidate atmospheric lights. Third, the haze image is decomposed into sky and non-sky regions with a histogram analysis based sky detection and segmentation method. And, color correction of the sky regions is carried out with a pixel distribution shifting based white balance method. The non-sky regions are smoothed with a patch shift based bilateral texture filtering process, which can preserve edges and eliminate redundant details. Fourth, a transmission estimation method based on hybrid filtering is proposed to eliminate the halo effects. Finally, the haze-free non-sky regions are recovered by solving the haze imaging model, which are then merged with the color-corrected sky regions to form the final haze-free image. Experimental results demonstrate that our algorithm can locate the valid atmospheric light, diminish the halo effects and improve the visibility remarkably, which outperforms the state-of-the-art image dehazing methods.

Keywords

Image dehazing Texture filtering  Patch shift  Atmospheric light Halo elimination 

Notes

Acknowledgments

This work is supported by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LY14F020004, the National Natural Science Foundation of China under Grant No. 61003188 and No. 61379075, the Talent Young Foundation of Zhejiang Gongshang University under Grant No. QZ13-9, the National Key Technology R&D Program under Grant No. 2014BAK14B01, the Zhejiang Provincial Commonweal Technology Applied Research Projects of China under Grant No. 2015C33071, and the Zhejiang Provincial Research Center of Intelligent Transportation Engineering and Technology under Grant No. 2015ERCITZJ-KF1. We are also benefited from the LIBSVM tool [15] provided by Chih-Jen Lin at National Taiwan University, and the PKU-EAQA datasets [14] provided by National Engineering Laboratory For Video Technology at Peking University.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Chunxiao Liu
    • 1
    Email author
  • Jinwei Zhao
    • 1
  • Yiyun Shen
    • 1
  • Yanggang Zhou
    • 1
  • Xun Wang
    • 1
  • Yi Ouyang
    • 1
  1. 1.School of Computer Science and Information EngineeringZhejiang Gongshang UniversityHangzhouChina

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