结合改进显著性检测与NSST的红外与可见光图像融合方法

Infrared and Visible Image Fusion Method Based on Improved Saliency Detection and Non-subsampled Shearlet Transform

针对当前基于显著性检测的红外与可见光图像融合方法存在目标不够突出、对比度低等问题,本文提出了一种结合改进显著性检测与非下采样剪切波变换(non-subsampled shearlet transform,NSST)的融合方法。首先,使用改进最大对称环绕(maximum symmetric surround,MSS)算法提取出红外图像的显著性图,并进一步通过改进伽马校正进行增强,同时应用同态滤波增强可见光图像。然后,对红外图像与增强的可见光图像进行NSST分解,利用显著性图指导低频部分进行融合;同时设定区域能量取大规则指导高频部分融合。最后,通过NSST逆变换重构融合图像。实验结果表明,本文方法在平均梯度、信息熵、空间频率和标准差上远优于其他7种融合方法,可以有效突出红外目标,提高融合图像的对比度和清晰度,并保留可见光图像的丰富背景信息。

To address the problems in the current infrared and visible image fusion method wherein targets are not prominent and contrast is low based on saliency detection,this paper proposes a fusion method by combining improved saliency detection and non-subsampled shearlet transform(NSST).First,the improved maximum symmetric surround algorithm is used to extract the saliency map of an infrared image,the improved gamma correction method is utilized to enhance the map,and the visible image is enhanced through homomorphic filtering.Second,the infrared and enhanced visible images are decomposed into low-and high-frequency parts through NSST,and the saliency map is used to guide the fusion of the low-frequency parts.Simultaneously,the rule of maximum region energy selection is used to guide the fusion of the high-frequency parts.Finally,the fusion image is reconstructed using the inverse NSST.The experimental results show that the proposed method is far superior to other seven fusion methods in terms of average gradient,information entropy,spatial frequency,and standard deviation.Thus,proposed method can effectively highlight the infrared target,improve the contrast and definition of fused images,and preserve rich background information of visible images.