[1]毕雪英,过 洁,潘金贵.强光源影响下的单幅图像去雾[J].常州大学学报(自然科学版),2015,(01):86-92.[doi:10.3969/ j.issn.2095-0411.2015.01.017]
　BI Xue-ying,GUO Jie,PAN Jin-gui.Single Image Dehazing with the Influence of Intense Light Source[J].Journal of Changzhou University(Natural Science Edition),2015,(01):86-92.[doi:10.3969/ j.issn.2095-0411.2015.01.017]
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强光源影响下的单幅图像去雾()
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常州大学学报(自然科学版)[ISSN:2095-0411/CN:32-1822/N]

卷:
期数:
2015年01期
页码:
86-92
栏目:
计算机与信息工程
出版日期:
2015-01-25

文章信息/Info

Title:
Single Image Dehazing with the Influence of Intense Light Source
作者:
毕雪英; 过 洁; 潘金贵
南京大学 计算机科学与技术系,江苏 南京 210023
Author(s):
BI Xue-ying; GUO Jie; PAN Jin-gui
Department of Computer Science and Technology,Nanjing University,Nanjing210023,China
关键词:
强光源影响;  去雾;  退化模型;  最小二乘法;  二维高斯拟合
Keywords:
influence of intense light source;  dehazing;  degradation model;  least square method;  2-D Gaussian fitting
分类号:
TP 391
DOI:
10.3969/ j.issn.2095-0411.2015.01.017
文献标志码:
A
摘要:
针对强光源影响所造成的非均匀大气情况,通过分析强光源对均匀大气雾天图像退化模型的影响,将大气光分量分为全局大气光常值分量和强光源影响分量,从而提出了一种针对强光源影响下的雾天图像去雾新方法。该方法首先自动获得图像中强光源的中心位置及拟合区间,然后利用最小二乘法计算强光源影响分量的二维高斯拟合,在输入雾图中去除强光源影响分量后得到大气光常值分量的估计值,进而根据暗通道先验知识估算大气透射率,最后利用所得参量对雾天图像进行去雾。实验结果表明,该去雾方法对户外强光源影响下的自然雾天图像复原效果较好。
Abstract:
In view of the inhomogeneous condition influenced by the intense light source,a novel single image dehazing approach is proposed for imagescontaining intensively emitted light source.This approach builds upon an imagedegradation model introduced via physically analyzing the illuminating effects of intense light source.In this paper,based on the experimental and observationaldiscovery,the atmospheric light is divided into global constant component and intense light component.Firstly,the location and fitting range of light source isobtained.Then,the 2-D Gaussian fitting function of intense light component iscalculated by using least square method,and the global constant component can beestimated after removing the strong light influence part in the input image.Thereafter,the atmospheric transmittance is estimated according to the dark channelprior knowledge on the light source removed image.Finally,the hazy image can berestored by the inverse process of the degradation model.The experimental results show that the new algorithm is effective for actual outdoor degraded images.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介:毕雪英(1986-),女,河北唐山人,硕士生。通讯联系人:潘金贵(1952-),E-mail:panjg@nju.edu.cn
更新日期/Last Update: 2015-01-25