基于YCbCr颜色空间的Retinex低照度图像增强方法研究  

Low-light Image Enhancement Method Using Retinex Method Based on YCbCr Color Space

作　　者：田会娟 蔡敏鹏[2,3] 关涛 胡阳 TIAN Hui-juan;CAI Min-peng;GUAN Tao;HU Yang(School of Electronics and Information Engineering,Tiangong University,Tianjin 300387,China;Engineering Research Center of Ministry of Education on High Power Solid Lighting Application System,Tianjin 300387,China;Tianjin Key Laboratory of Advanced Electrical Engineering and Energy Technology,School of Electrical Engineering and Automation,Tiangong University,Tianjin 300387,China)

机构地区：[1]天津工业大学电子与信息工程学院,天津300387 [2]大功率半导体照明应用系统教育部工程研究中心,天津300387 [3]天津工业大学电气工程与自动化学院,天津市电工电能新技术重点实验室,天津300387

出　　处：《光子学报》2020年第2期167-178,共12页Acta Photonica Sinica

基　　金：国家自然科学基金(No.61504095)~~

摘　　要：针对Retinex理论的低照度图像增强算法中光照图像估计问题,提出一种基于YCbCr颜色空间的低照度图像增强方法.该方法将原始低照度图像从RGB(Red Green Blue)颜色空间转换到YCbCr颜色空间,提取该空间中Y分量构建为原始光照图像分量L1(x,y),并对L1(x,y)进行Gamma校正得到增强的光照图像分量L2(x,y),经Retinex模型得到增强图像R(x,y),采用多尺度细节增强方法对图像R(x,y)进行细节增强,得到最终增强图像Re(x,y).实验结果表明,所提方法不仅能有效提升亮度,避免亮度和色彩失真,增强了图像的细节信息并获得了更好的视觉效果,而且运行速度快.Aiming at the problem of illumination image estimation in low-light image enhancement algorithm of the Retinex model,a low-light image enhancement method based on YCbCr color space is proposed.The original low-light image is transformed from RGB(Red Green Blue)color space to YCbCr color space.The Y component in YCbCr color space is extracted and the initial illumination map L1(x,y)is constructed.The enhanced illumination image L2(x,y)is obtained by the gamma transformation of L1(x,y),the enhanced image R(x,y)is obtained according to the Retinex model,and we use a multi-scale approach to boost the details of the image R(x,y)and obtain the final enhanced image Re(x,y).The experimental results show that,the method can not only effectively improve the brightness of the low-light images,enhance the details of the image,obtain a better visual effect with fewer color and lightness distortions,but also has a faster running speed.

关 键 词：Retinex模型 图像增强 光照估计 GAMMA校正 细节增强 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]