Abstract: In response to the issues of thermal target loss and detail degradation in the fusion of infrared and visible-light images, a novel fusion algorithm is proposed based on image enhancement and L1-L0 decomposition. First, an enhancement algorithm for visible light images (GTGA) is introduced to enhance visible light textures by processing individual layers decomposed through rolling-guided filtering, thereby improving overall brightness using enhanced adaptive gamma correction. Subsequently, an algorithm for directional infrared target extraction (MIG) is presented. Finally, the L1-L0 decomposition method is employed to separate infrared and visible-light images into base and two detail layers. A fusion method based on guided filtering was employed for weight extraction of base layer images. For the primary detail layers, contrast energy PCA was introduced for fusion, whereas a fusion strategy based on visual feature mapping was adopted for the secondary detail layers. Finally, the fused base layers, detail layers, and infrared targets were reconstructed to obtain the final fused image. To validate the effectiveness of the algorithm, comparisons were conducted with nine other infrared and visible light image fusion algorithms. Experimental results demonstrate that the algorithm achieves superior performance in both subjective and objective evaluations compared with other fusion algorithms.