基于改进的BEMD的红外与可见光图像融合方法

将红外图像与可见光图像融合在一起,可增强视觉效果,使人产生更完整的场景感知。基于二维经验模态分解(Bidimensional Empirical Mode Decomposition,BEMD)的图像融合方法运行时间较长,因此,文中提出了一种基于改进的二维经验模态分解的红外与可见光图像快速自适应融合方法,采用顺序统计滤波器和高斯滤波器直接生成均值包络曲面,从而加速图像的分解过程。首先,将可见光图像转化到HIS(Hue-Intensity-Saturation)颜色空间;然后,用改进的BEMD对强度分量I和红外图像进行分解,生成高频分量和低频分量,高频分量和低频分量分别采用自适应局部加权融合规则和算术平均融合规则;最后,将强度分量I与红外图像的融合结果图经过逆HIS变换到RGB颜色空间,从而得到融合图像。仿真实验表明,该融合算法不仅运行速度快,而且融合效果最佳,最大程度地保留了红外图像的边缘细节特征和可见光图像的光谱信息。

Fusion of Infrared and Color Visible Images Based on Improved BEMD

Image fusion between the infrared and color visible images can enhance vision and improve the situation awareness.A direct use of the bidimensional empirical mode decomposition(BEMD)method for image fusion suffers from a high computation cost.Therefore,this paper proposed an improved BEMD for a fast and adaptive image fusion of infrared and color visible images.It is achieved by using order statistics filter and modified Gaussian filter to calculate the mean envelope directly,so as to accelerate the sifting process within the original BEMD.Firstly,the color visible image is transformed into IHS components.Secondly,the intensity component and the infrared image are decomposed into high frequency components and the low frequency components by means of the improved BEMD.Then,the adaptive local weighted fusion rule and the arithmetic mean rule are respectively applied to fuse the high frequency components and the low frequency components.Finally,the new intensity is transformed back into RGB.The proposed image fusion scheme is not only fast but also able to achieve the best fusion result,which merges edge details in the infrared image and the spectral information in the color visible image well.