Infrared and Visible Image Fusion Using Visual Saliency Sparse Representation and Detail Injection Model

Abstract

The goal of infrared and visible image fusion is to generate an integrated image which can simultaneously preserve more visible detail information and prominent information from the input images. In order to achieve this goal, a novel infrared and visible fusion method based on visual saliency sparse representation and detail injection model (DIM) is presented. The proposed fusion method contains four steps. The first step is to decompose the source images into base layers and detail layers through the proposed multiscale decomposition method, which has the advantages of scale awareness and high-edge-preservation efficiency. Then, we design a novel subfusion rule called visual saliency sparse representation to get the fused base layer. Third, a DIM is developed to fuse the detail layers. In this model, we first employ a “max-absolute” scheme to obtain prefused images, which are then used as the model items for participating in the fusion process of the detail layers. Finally, the target-merged image is achieved through an inverse multiscale decomposition method. The experimental results demonstrate that the proposed method can not only preserve the details and significant information of source images but also enhance the brightness of the fused image, when compared to other state-of-the-art fusion methods.