摘要
针对在暗原色先验理论下天空区域的透射率预估过小以及大气光值求取受白色物体干扰的缺陷,提出一种改进的基于暗原色先验图像去雾算法。首先将归一化的亮度-饱和度差值图像与粗略透射率逐一比较,取最大值作为新的透射率;其次通过晕光算子操作获取大气光值描述区域,以获得正确的大气光值;最后根据大气散射模型复原图像。实验结果表明,与其他典型去雾算法相比,所提的算法处理时间较短,复原图像细节突出,色彩真实无失真。
To tackle the weakness that the transmission of sky area is underestimate under the dark channel prior theory and the atmospheric light value estimation is affected by white object,a dehazing method for single image based on dark channel prior is proposed. Firstly,the maximum of every pixel between the normalized brightness-saturation difference and the original transmission was regarded as a new transmission map. Then,the accurate atmospheric light was obtained by atmospheric light description area,which was acquired by halo operation. Finally,a clear image could be reconstructed according to the atmospheric scattering model. The experimental results demonstrated that,compared with other representative dehazing algorithm,the proposed algorithm can achieve a faster processing speed and provide more detailed restored image with good color effect.
作者
杨斌
林志贤
郭太良
Yang Bin;Lin Zhixian;Guo Tailiang(College of Physics and Information Engineering,Fuzhou University,Fuzhou 350000,Chin)
出处
《信息技术与网络安全》
2018年第5期75-79,共5页
Information Technology and Network Security
基金
国家重点研发计划(2016YFB0401503)
福建省科技重大专项(2014HZ0003-1)
广东省科技重大专项(2016B090906001)
福建省资助省属高校专项课题(JK2014002)
关键词
亮度-饱和度差值
晕光算子
暗原色先验
去雾
brightness-saturation difference
halo operator
dark channel prior theory
dehazing
