摘要
针对基于暗通道先验去雾算法易产生图像偏暗、细节信息丢失等现象,提出了一种基于引导系数加权和自适应图像增强去雾算法.首先,对原导向滤波方法进行采样和引导系数加权,快速得到精细化的透射率;然后,利用K-均值聚类将原图像标定为亮色和非亮色区域,约束透射率和大气光值,达到图像噪声抑制和大气光值优化的效果;最后,结合大气散射模型恢复图像,并利用自适应线性对比度增强方法对恢复后的图像进行优化.实验结果表明,与其他代表性去雾方法相比,由本文算法所获得的去雾图像不仅能克服图像失真、细节丢失等问题,同样在主观指标上和客观指标上都能取得较好的结果.
Aiming at the dim and detail information loss problem that occurred in dark channel prior dehazing algorithm,a haze removal algorithm based on guided-coefficient-weighted and adaptive image enhancement method was proposed in this paper.Firstly,in order to efficiently refine the transmission,a sampling and guided-coefficientweighted method was applied to correct the original guided filter algorithm.Then,the K-means clustering method was adopted to mark the bright and non-bright regions according to the original image.It was used to restrict the range of transmission and atmospheric light,thus the noise can be suppression effectively and the atmospheric light can be well optimized.Finally,the image was restored by the atmospheric scattering model and optimized by the adaptive linear contrast enhancement method.Experimental results demonstrated that the proposed method can not only overcome the problem of image distortion and detail information loss,but also more efficient than conventional dehazing methods.
作者
何立风
周广彬
姚斌
赵晓
李笑
HE Li-feng;ZHOU Guang-bin;YAO Bin;ZHAO Xiao;LI Xiao(School of Electronic Information and Artificial Intelligence,Shaanxi Univ.of Science and Technology,Xi′an 710021,China;Faculty of Information Science and Technology,Aichi Prefectural Univ.,Aichi 480-1198,Japan)
出处
《微电子学与计算机》
北大核心
2020年第9期73-77,82,共6页
Microelectronics & Computer
基金
国家自然科学基金面上项目(61971272)
国家自然科学基金青年基金项目(61603234
61601271)。
关键词
暗通道先验
图像去雾
导向滤波
引导系数加权
聚类
图像增强
DCP
image dehazing
guided filtering
guided-coefficient-weighted
clustering
image enhancement
