Adaptive fractional multi-scale edge-preserving decomposition and saliency detection fusion algorithm

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.