Integrated Enhancement Algorithm for Hazy Image Using Transmittance as Weighting Factor

Julang JIANG; Wei SUN; Zhendong WANG; Zhu ZHU; Jiangyun ZHENG

doi:10.11999/JEIT171032

Volume 40 Issue 10

Sep. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(10): 2388-2394

Julang JIANG, Wei SUN, Zhendong WANG, Zhu ZHU, Jiangyun ZHENG. Integrated Enhancement Algorithm for Hazy Image Using Transmittance as Weighting Factor[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2388-2394. doi: 10.11999/JEIT171032

Citation:

Julang JIANG, Wei SUN, Zhendong WANG, Zhu ZHU, Jiangyun ZHENG. Integrated Enhancement Algorithm for Hazy Image Using Transmittance as Weighting Factor[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2388-2394. doi: 10.11999/JEIT171032

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Integrated Enhancement Algorithm for Hazy Image Using Transmittance as Weighting Factor

doi: 10.11999/JEIT171032

1.
School of Physics and Electrical Engineering, Anqing Normal University, Anqing 246133, China
2.
Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China

Funds: The National Natural Science Foundation of China (61701006), The Anhui Natural Science Foundation (1708085QF147), The Natural Science Key Foundation of Anhui Education Committee (KJ2015A234)

Received Date: 2017-11-02
Rev Recd Date: 2018-07-02

Available Online: 2018-07-30

Publish Date: 2018-10-01

Abstract

Abstract

Hazy image enhancement has important practical significance. Since the existing haze removal algorithms have disadvantages in improving the global contrast of images, a novel hazy image enhancement algorithm is presented by integrating advantages of haze removal and histogram equalization. Firstly, the hazy image is processed respectively using guided filtering-based dark channel prior algorithm and HSV space-based histogram equalization algorithm. Then, the output image is obtained by fusing the above two results using weighting factor which is constructed by the revised transmittance map. Simulation results show that the algorithm has higher standard deviation, average gradient and information entropy than the present hazy removal algorithm, and shows better result of global and local contrast. The running time of the algorithm mainly depends on the process of haze removal, which can meet the real-time requirements for normal size image.
- Image fusion,
- Hazy image,
- Transmittance,
- Dark channel prior,
- Histogram equalization

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References(17)

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