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A Study of Single Image Haze Removal Using a Novel White-Patch RetinexBased Improved Dark Channel Prior Algorithm

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1 Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan
2 Department of Electrical Engineering, National Central University, Taoyuan City 32001, Taiwan

* Corresponding Author: Yao-Liang Chung, email

Intelligent Automation & Soft Computing 2020, 26(2), 367-383. https://doi.org/10.31209/2020.100000206

Abstract

In this study, we introduce an algorithm which is based on a series of wellknown algorithms and mainly uses an improved dark channel prior algorithm and the White-Patch Retinex algorithm (both are heterogeneous algorithms) in order to effectively remove the haze from a single image. When used in conjunction with a heterogeneous architecture, the value of the algorithm becomes even greater. With an effective design and a novel procedure, the proposed algorithm can not only restore a clear image, but also solve the halo effect, color distortion, and long operating time issues resulting from the dark channel prior. Rich experimental results (visually and numerically) confirm that the performance and effectiveness of the new algorithm are better than those of the dark channel prior algorithm, indicating the applicability of the new algorithm.

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APA Style
Chung, Y., Chung, H., Chen, Y. (2020). A study of single image haze removal using a novel white-patch retinexbased improved dark channel prior algorithm. Intelligent Automation & Soft Computing, 26(2), 367-383. https://doi.org/10.31209/2020.100000206
Vancouver Style
Chung Y, Chung H, Chen Y. A study of single image haze removal using a novel white-patch retinexbased improved dark channel prior algorithm. Intell Automat Soft Comput . 2020;26(2):367-383 https://doi.org/10.31209/2020.100000206
IEEE Style
Y. Chung, H. Chung, and Y. Chen, “A Study of Single Image Haze Removal Using a Novel White-Patch RetinexBased Improved Dark Channel Prior Algorithm,” Intell. Automat. Soft Comput. , vol. 26, no. 2, pp. 367-383, 2020. https://doi.org/10.31209/2020.100000206



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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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