Adaptive single image dehazing method based on support vector machine |
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| Affiliation: | 1. College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China;2. Yellow River Conservancy Technical Institute, Kaifeng 475000, China;1. Department of Electronic Engineering, National Taipei University of Technology, Taipei City 10608, Taiwan;2. Department of Communication Engineering, National Central University, Taoyuan City 320, Taiwan;1. National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China;2. Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China;3. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China;4. Wuhan University of Technology, Wuhan 430070, China;1. Haian Senior School of Jiangsu Province, Nantong 226600, China;2. College of Physical Education, China University of Mining and Technology, Xuzhou 221000, China;1. Department of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk, South Korea;2. Engineering Research Center on Cloud Computing & Internet of Things and E-commerce Intelligence of Fujian Universities Quanzhou Normal University, No. 398, Donghai Street, Fengze District, Quanzhou 362000, China;3. School of Economics and Management, Xinyu University, No. 2666, Yangguang Street, Xinyu 338004, China |
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| Abstract: | A dehazing method often only shows good results when processing the image for a certain haze concentration. So an adaptive hazy image dehazing method based on SVM is proposed. The innovation points are as follows: Firstly, combining the characteristics of the degraded images of haze weather, the dark channel histogram and texture features of the input images are extracted to form the feature vectors. These are trained by supervised learning through SVM algorithm to realize automatic binary classification of images; Secondly, the defined dehazing methods are called to process the classified result as a hazy image and the same quality evaluation indexes are used to evaluate each image output by different dehazing methods. Then, it outputs the highest evaluation image after haze removal. Finally, the output image is classified again by SVM until the image reaches the clearest it can be. The experimental results show that the proposed algorithm exhibits good contrast, brightness and color saturation from the visual effect. Also the scene adaptability and robustness of the algorithm are improved. |
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| Keywords: | SVM Adaptive dehazing Automatic binary classification Quality evaluation index |
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