LU Xinxuan， YANG Yan
（School of Electronic and Information Engineering， Lanzhou Jiaotong University， Lanzhou 730070, China）
Abstract： Aiming at the inaccurate transmission estimation problem of dark channel prior image dehazing algorithm in the sudden change area of depth of field and sky area, a dehazing algorithm using adaptive dark channel fusion and sky compensation is proposed. Firstly, according to the characteristics of minimum filtering of large window scale and small window scale in the dark channel prior, the fused dark channel is obtained by weighted fusion of the approximate depth of field relationship, thus obtaining the primary transmission. Secondly, use the down-sampling to optimize the primary transmission combined with gray scale image of haze image by fast joint bilateral filtering, then restore the original image size by up-sampling, and the compensation of the Gaussian function is used in the sky area to obtain corrected transmission. Finally, the improved atmospheric light is combined with atmospheric scattering model to recover haze-free image. Experimental results show that the algorithm can recover a large amount of detailed information of the image, obtain high visibility, and effectively eliminate the halo effect. At the same time, it has a better recovery effect on bright areas such as the sky area.
Key words： dark channel prior； approximate depth of field； weighted fusion； fast joint bilateral filtering； Gaussian function compensation
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自适应暗通道融合和天空补偿的去雾算法
陆鑫璇， 杨 燕
（兰州交通大学 电子与信息工程学院， 甘肃 兰州 730070）
摘 要： 针对暗通道先验图像去雾算法在景深突变区域和天空区域出现透射率估计不准确问题， 提出一种自适应暗通道融合和天空补偿的去雾算法。 首先， 根据大窗口尺度和小窗口尺度的最小值滤波在暗通道先验的特点， 将两者通过近似景深关系加权融合获得融合暗通道， 从而获得初级透射率； 其次， 对初级透射率进行下采样， 用有雾图像的灰度图进行快速联合双边滤波， 再使用上采样恢复原图像尺度大小， 并在天空区域采用高斯函数补偿优化得到修正的透射率图； 最后， 通过改进的大气光值， 结合大气散射模型恢复出无雾图像。 实验结果表明， 该算法能够恢复图像大量细节信息， 获得高可视度， 并有效消除光晕效应， 同时在天空等明亮区域有较好的恢复效果。
关键词： 暗通道先验； 近似景深； 加权融合； 快速联合双边滤波； 高斯函数补偿
引用格式： LU Xinxuan， YANG Yan. Dehazing algorithm using adaptive dark channel fusion and sky compensation. Journal of Measurement Science and Instrumentation, 2021, 12（2）： 177-187. DOI： 10.3969／j.issn.1674-8042.2021.02.007
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