A Far and Near Scene Fusion Defogging Algorithm Based on the Prior of Dark-Light Channel

被引：4
作者：
Gao T. [1 ]
Liu M. [1 ]
Chen T. [1 ]
Wang S. [1 ]
Jiang S. [1 ]
机构：
[1] School of Information Engineering, Chang'an University, Xi'an
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2021年 / 55卷 / 10期
关键词：
Image correction; Image defogging; Image segmentation; Light channel; Mixed dark channel;
D O I：
10.7652/xjtuxb202110009
中图分类号：
学科分类号：
摘要：
A far and near scene fusion defogging algorithm based on the prior of dark-light channel is proposed to solve the problem that traditional dark channel is not suitable for large sky area and it is easy to cause the distortion of dehazed image. Firstly, an improved two-dimensional Otsu image segmentation algorithm is utilized to mix the dark channels in the close and distant areas, and the adaptive adjustment parameters of the mixed dark channels are calculated based on the optimal objective quality evaluation index for the close and distant areas. Secondly, aiming at the problem that atmospheric light is not uniform and constant in real physical scenes, a dark-light channel fusion model is established to calculate the atmospheric light map. Furthermore, in order to improve processing speed, the grayscale image corresponding to the original image is selected as a guide image to refine the transmittance image without reducing restoration quality. Finally, the brightness/colour compensation model based on visual perception is used for image correction to improve the contrast and colour saturation of the restored image. Experimental results show that the proposed algorithm achieves the best results from both subjective and objective perspectives, in which the objective index PSNR is 24.04% higher than that of He's algorithm on average. It is concluded that the image recovered by the proposed algorithm is clearer, with more obvious details and structure, and is more suitable for human eyes to observe, which verifies the effectiveness of the algorithm. © 2021, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
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页码：78 / 86
页数：8
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