基于显著导向的可见光与红外图像融合算法

为了克服当前较多可见光与红外图像融合方法主要利用图像能量特征来融合图层内容，忽略了图像的显著信息，导致融合图像中存在对比度较低等不足，本文以图像的显著信息为导向来融合可见光与红外图像。首先，借助L0和L1范数来设计平滑变换，对可见光与红外图像进行分解，获取边缘等特征保持较好的基础层和细节层。然后，利用频率调谐(FT)方法，获取红外图像中的显著信息，并以此为依据，建立基础层的融合模型，获取融合基础层图像。通过图像的信息熵特征，构建细节层的融合准则，从不同细节层图像的信息关联性出发，获取融合细节层图像。通过对融合细节层和融合基础层图像进行求和操作，输出融合图像。最后，在TNO数据集上进行了测试，结果显示，与当前技术相比，本文算法拥有更高的融合效果，可以更好地凸显目标信息与保持纹理细节。

Visible and infrared image fusion algorithm based on saliency guidance

The current image fusion methods mainly use image energy features to fuse layer content, and ignore the significant information of image, resulting in low contrast in the fusion image. In this paper, a method is proposed, which will fuse visible and infrared images based on the significant information of image. Firstly, a smooth transform is designed to decompose the visible and infrared images by using the L0 and L1 norms, and obtain the base layer and detail layer images with good edge features. Then, by using the frequency tuning method, the significant information in the infrared image is obtained to establish the fusion model of the base layer image, and get the fusion base layer image. Through the information entropy features of image, the fusion model of detail level image is constructed, and the fusion detail level image is obtained from the information relevance of different detail level images. The fusion image is obtained by summing the fusion detail layer image and the fusion base layer image. Experimental results show that this algorithm can better fuse visible and infrared images than current algorithms; its fusion results can not only highlight the target information, but also have better contrast.