Abstract: To solve the insufficient background information in the fusion image and the problem that the infrared target salience and visible image detail preservation hard to achieve at the same time in the fusion process, in this paper, an infrared and visible light fusion method was proposed, which combined image enhancement and saliency detection on the basis of latent low rank decomposition, and used gradient guided filtering to reconstruct the fusion decision image. Firstly, the image enhancement algorithm was used to enhance the visible image to obtain a better performance of detail and contour, and the visual saliency detection algorithm (VSM) was used to processes the infrared image to obtain the first weight map. Secondly, the latent low-rank representation (LatLRR) decomposition was performed to obtain the detail layer and the base layer, the decomposed detail layer was taken as a parameter introduced into the gradient domain guidance filtering algorithm to filter the initial weight map, and got the improved weight map which preserving details and contour more accurately. Thirdly, the primary weight and secondary weight were used as the fusion strategy respectively to reconstruct the base layer and the detail layer. Finally, the weighted average algorithm was used to fuse the reconstructed details and base layer to obtain the final result. Experiments showed that the proposed method got better performance in preserving the background detail information, improving the infrared target's salience and the identificational degree of contour features. It also got better results in quality indexs such as average gradient,visual information fidelity,mutual information and so on.