Image fusion expands the space–time scope of the detection target by making full use of the information complementarity between images, which is useful for automatic target recognition. An adaptive fractional multi-scale edge-preserving decomposition based on the weighted least square framework is proposed to fuse infrared (IR) and visible (VIS) images. It decomposes images into the base layer and the detail layer. The saliency map is obtained based on adaptive fractional saliency detection of the IR image. This map is beneficial to fuse the base layer and highlights the saliency information significantly. Then, the detail layer is fused by classical choose-max. The experiments indicate that the proposed method outperforms state-of-the-art fusion methods in extracting the target and preserving background information of the IR and VIS images.

图像融合通过充分利用图像之间的信息互补性来扩展检测目标的时空范围，这对于自动目标识别很有用。提出了一种基于加权最小二乘框架的自适应分数阶多尺度边缘保留分解算法，以融合红外图像和可见光图像。它将图像分解为基础层和细节层。基于红外图像的自适应分数显着性检测获得显着性图。该图有利于融合基础层并显着突出显示显着性信息。然后，细节层通过经典的select-max融合。实验表明，该方法在提取目标和保留红外和可见光图像的背景信息方面优于最新的融合方法。