摘要
夜间有雾图像光照不均、对比度较低且色偏严重。现有的去雾算法主要是针对白天图像,并不适用于夜间场景,夜间图像去雾难度较大。该文通过深入分析夜间有雾图像的成像特点,提出了基于低通滤波和多特征联合优化的夜间图像去雾算法。针对夜间图像环境光照不均匀问题,提出先对图像进行低通滤波,然后对其低频分量三通道利用最小-最大值滤波估计局部环境光;针对目前白天去雾算法先验不适用于夜间图像,提出结合图像对比度、饱和度和信息熵特征,构建多特征联合优化函数估计透射率;针对夜间图像存在非一致色偏问题,提出非重叠块局部Shade of Gray算法进行颜色校正。实验结果表明:所提算法去雾图像的主观视觉效果较好,且对比度和色偏程度两方面客观评价指标整体优于其他对比算法。该算法能够有效去除夜间图像雾气,提高图像的对比度,恢复更多的细节信息,且颜色自然,视觉效果理想。
Nighttime hazy image usually has the non-uniform illumination,low contrast and serious color deviation.The existing dehazing methods are mainly proposed for daytime images,which don＇t fit well with the conditions of most nighttime hazy scenes.Nighttime image dehazing is more difficult.We explore the imaging characteristics under nighttime conditions and propose a new nighttime image dehazing method based on low-pass filtering and joint optimization of multi-feature.Firstly,in order to handle the non-uniform illumination of nighttime scenes,the image is filtered by the low-pass filtering.And then the minimum-maximum filtering is applied to the low frequency components to estimate the local atmospheric light.Secondly,for the current daytime dehazing algorithm prior is not suitable for nighttime image,an effective transmission estimation method is presented based on the joint optimization of multi-feature which combines contrast,saturation and information entropy.Finally,for the non-uniform color deviation exists in nighttime images,the non-overlapping blocking local Shade of Gray is proposed.Experimental results demonstrate that the proposed algorithm has a good subjective visual effect,and the objective evaluation indexes are superior to other algorithms in contrast and color deviation degree.The proposed algorithm can significantly remove haze,improve the contrast and recover more details with the natural color and better visual effect.
作者
杨爱萍
赵美琪
王海新
鲁立宇
Yang Aiping;Zhao Meiqi;Wang Haixin;Lu Liyu(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2018年第10期159-168,共10页
Acta Optica Sinica
基金
国家自然科学基金(61372145
61472274
61771329)
关键词
图像处理
夜间图像去雾
低通滤波
局部环境光
多特征联合优化
image processing
nighttime image dehazing
low-pass filtering
local atmospheric light
joint optimization of multi-feature
