Image fusion aims to improve image recognition and detail richness of images by fusing two or more images into a new image using a specific algorithm. This paper proposes an image fusion algorithm based on edge-guided filtering enhancement and graph wavelet transform (GWT) to solve problems of traditional image fusion methods such as lack of details and unclear edge textures. Firstly, edge-guided filtering is used to pre-process and enhance low light images. Then, GWT is used to perform multi-scale decomposition on infrared and low light images, obtaining their respective low-frequency and high-frequency sub-band images. For low-frequency sub images, rolling guidance filtering (RGF) is used for decomposition to obtain the base layer and detail layer. The base layer is fused using visual saliency map (VSM), while the detail layer is fused using the maximum absolute (MA) principle; For high-frequency sub images, fusion is performed using maximum regional energy. Finally, GWT inverse transformation is performed on the fused low-frequency and high-frequency sub-band images to obtain the final fusion result. The experimental results on public datasets demonstrate the proposed method is superior to compared algorithms in terms of high subjective visual effects, and texture information preservation and edge details.