基于BEMD改进的视觉显著性红外和可见光图像融合

Infrared and Visible Image Fusion Based on BEMD and Improved Visual Saliency

针对视觉显著性融合过程中目标对比度低,图像不够清晰的问题,本文提出一种基于二维经验模态分解(bidimensional empirical mode decomposition,BEMD)改进的Frequency Tuned算法。首先利用BEMD捕获红外图像的强点、轮廓信息用于指导生成红外图像的显著性图,然后将可见光图像和增强后的红外图像进行非下采样轮廓波变换(nonsubsampled contourlet transform,NSCT),对低频部分采用显著性图指导的融合规则,对高频部分采用区域能量取大并设定阈值的融合规则,最后进行逆NSCT变换生成融合图像并进行主观视觉和客观指标评价,结果表明本文方法实现了对原图像多层次、自适应的分析,相较于对比的方法取得了良好的视觉效果。

Aiming at the problems of low target contrast and insufficiently clear images in the process of visual saliency fusion,this paper proposes an improved frequency Tuned algorithm based on bi-dimensional empirical mode decomposition(BEMD).First,the strong points and contour information of the infrared image captured by BEMD is used to guide the generation of saliency maps of the infrared image.Then,the visible image and the enhanced infrared image are subjected to a non-subsampled contourlet transform(NSCT).The saliency map-guided fusion rule is used for the low-frequency part.The high-frequency part is used to set the area energy to be large and rely on the threshold value rules.Finally,the inverse NSCT transform is used to generate a fused image and subjective visual and objective index evaluations are performed to it.The results show that the method in this paper achieves a multi-level and adaptive analysis of the original image,and achieves good vision compared to the contrast methods.