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Single image dehazing using kernel regression model and dark channel prior

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Abstract

Haze is one of the major factors that degrade outdoor images, and dehazing becomes an important issue in many applications. In order to address the problems of being unsmooth and the absence of neighbor information for the transmission estimation under Dark Channel Prior (DCP) framework, we proposed a new improved method using Kernel Regression Model (KRM) on local neighbor data. Firstly, the initial transmission in atmospheric light model is estimated by DCP. Secondly, the transmission is refined according to KRM. Experimental results on synthetic and real images show that our method can address this problem and has better dehazing results than several state-of-the-art methods.

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Acknowledgments

The authors of this paper wish to thank the referees for their valuable suggestions. This work is supported by the Science and Technology Program of suzhou in China under Grant No. SYG201409, Natural Science Foundation of Jiangsu Province in China (No. BK20130529), Natural Science Foundation of the Jiangsu Higher Education Institutions in China (No. 3KJB520001).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu Province, China

    Cong-Hua Xie & Wen-Hao Ying

  2. School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu Province, China

    Wei-Wei Qiao

  3. School of Computer Science and Telcommunication Engineering, Zhenjiang, 212013, Jiangsu Province, China

    Zhe Liu

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  1. Cong-Hua Xie
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  2. Wei-Wei Qiao
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  3. Zhe Liu
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  4. Wen-Hao Ying
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Correspondence to Cong-Hua Xie.

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Xie, CH., Qiao, WW., Liu, Z. et al. Single image dehazing using kernel regression model and dark channel prior. SIViP 11, 705–712 (2017). https://doi.org/10.1007/s11760-016-1013-3

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  • DOI: https://doi.org/10.1007/s11760-016-1013-3

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