This study proposes a fusion algorithm for infrared and visible-light images based on image enhancement and multiscale decomposition to address the low-contrast and unclear edge contours of conventional multiscale transformation methods in the fusion process of infrared and visible-light images. First, an image-enhancement algorithm based on guided filtering is used for visible-light images to improve the overall contrast and visibility. Second, an improved rolling-guided filter is used to decompose the enhanced visible and infrared images into base layers and detail layers of different scales. Subsequently, saliency analysis is performed on the basic and detailed layers, a saliency map is constructed, and the weight map is calculated. Finally, the weighted average fusion of the base and detail layers is performed using the weight graph, and the fusion of the base and detail layers is added to obtain the final fusion result. The subjective image-quality analysis and seven evaluation indices are compared using eight multiscale fusion methods. Experimental results show that the proposed method not only preserves the edge contour of the source image and improves the overall contrast and clarity of the fusion result but also reduces artifacts.