In response to concerns about the insufficient visibility of target information and loss of details in traditional multiscale fusion methods for infrared and visible images, this paper proposed a hybrid multiscale decomposition fusion method based on anisotropic guided filtering. Initially, an adaptive image enhancement method based on texture contours was introduced to improve visible images by simultaneously enhancing brightness, contrast in dark regions, and texture details. Subsequently, the brightness layer of the source image was extracted using the edge-preserving smoothing property of anisotropic guided filtering. The difference layer was decomposed into a base layer, a small-scale detail layer, and multiple levels of large-scale detail layers via Gaussian filtering. The fusion rule for the brightness layer employed an absolute maximum value approach, and a fusion method that combined visual saliency with least squares optimization was proposed for the base layer. The small-scale detail layer adopted a fusion strategy based on modified Laplacian energy, and the large-scale detail layers employed a composite fusion strategy based on local variance and spatial frequency. Finally, the fusion image was reconstructed by combining the merged layers. Compared with nine other classic and advanced methods, the proposed method performs well in both subjective and objective analyses.