摘要
针对红外与可见光成像传感器的物理特性,提出了一种基于二代Curvelet变换的图像融合算法.首先对原始图像分别进行快速离散Curvelet变换,得到不同尺度与方向下的子带系数.对低频子带系数,根据红外图像的目标特性与可见光图像的细节信息确定其融合权值;对不同尺度与方向下的高频子带系数,采用基于局部区域能量匹配的融合规则.最后经Curvelet逆变换得到融合结果.实验结果表明,该算法可以有效地综合可见光与红外图像中的重要信息,其融合结果较典型的基于塔式分解与基于小波变换的图像融合算法,在主观视觉效果与客观评价指标上均有所改善.
Aiming at the physical characteristics of infrared and visible imaging sensors,a novel image fusion algorithm based on the second generation Curvelet transform was proposed.Firstly,the fast discrete Curvelet transform was performed on the original images respectively to obtain the subband coefficients at different scales and in various directions.Then for low frequency subband coefficients,the fusion weights were determined by the target characteristics of infrared image and the detail information of visible image;while for high frequency subband coefficients,a fusion rule based on local-region energy matching was employed.Finally,the fusion results were obtained through the inverse Curvelet transform.Experimental results show that the proposed algorithm can effectively integrate important information from infrared and visible images,and the obtained results are better than those of pyramid-based or wavelet-based algorithms.
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2009年第4期254-258,共5页
Journal of Infrared and Millimeter Waves
基金
国家自然科学基金(60773044)
总装备部重点基金(6140525)资助项目
