自适应参考图像的可见光与热红外彩色图像融合算法  

Visible and Infrared Thermal Image Fusion Algorithm Based on Self-Adaptive Reference Image

作　　者：刘佳妮[1] 金伟其[1] 李力[1] 王霞[1] 

机构地区：[1]北京理工大学光电成像技术与系统教育部重点实验室,北京100081

出　　处：《光谱学与光谱分析》2016年第12期3907-3914,共8页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金重点项目(61231014);教育部博士学科点专项科研基金项目(20131101130002);"十二五"总装重点预研项目(40405030302)资助

摘　　要：可见光与红外热图像的彩色图像融合技术是现今国内外高性能夜视技术发展的重要方向之一,该技术有效提高了人们对目标的探测和场景理解能力。目前常用的色彩传递算法多属于基于单幅参考图像的全局色彩传递算法,彩色融合图像的色调受到参考图像的影响较大,在实际应用中难以保证对各类场景的适应性。针对常规YUV空间色彩传递彩色图像融合算法的环境适应性问题,通过对植物、城镇和海天三类典型场景的分类与统计,发现了典型场景在UV通道的均值和标准差具有的较为明显的分类特性,由此提出了一种基于UV通道均值和标准差的自适应参考图像构造方法,使得可见光与热红外彩色图像融合算法具有较常规算法更好的环境适应性,融合图像的色彩具有较好的自然感,且算法处理量较小,对现有实时硬件融合处理算法的运算速度影响不大,是一种环境适应性强的自然感彩色融合处理算法。The technology of fusing visible images and infrared thermal images is one of the most important researching fields in terms of high-performance night vision technology home and abroad Using this kind of technology can efficiently improve people＇s ability of target-detecting and scene＇s comprehension.The commonly used color transfer algorithm belongs to single reference image＇s global color transfer,which results in huge influence on the color of fusion image.Thus,this kind of method cannot adapt to different kinds of scenes.In this essay,we focus on environmental adaptation problems of regular color transfer in color image fusion algorithm base on YUV color space.After analyzing the mean and standard deviation of typical scenes＇ UV channel,we find that it has obvious classified characteristics.As a result,we propose a method of constructing adaptive reference image based on mean and standard deviation of U and V channels,which makes visible and infrared thermal image fusion algorithm better adapt to environment and fusion images＇ color is natural.Furthermore,the handling capacity is small and it exerts little impact on the speed of algorithm real-time hardware processing.To conclude,it is a kind of practical natural color fusion processing algorithm.

关 键 词：可见光 热成像 彩色图像融合 参考图像 自适应 

分 类 号：TN215[电子电信—物理电子学]