Different types of detectors have different imaging mechanisms, and the information represented by the image is also different in some ways, which results in the information of a scene cannot be completely descripted through a single image. Therefore, it is an important technology to extract complementary information of multi-source images, remove redundant information and synthesize a composite image which can express scene accurately and completely. Image fusion is an effective solution to this kind of problem. In this paper, an infrared and visible image fusion based on multi-layer image decomposition is proposed. Firstly, using the edge-preserving characteristics of weighted mean curvature filtering and the smoothing characteristics of Gaussian filtering, a multi-layer image decomposition model was constructed. Secondly, the source images were decomposed into small-scale layers, large-scale layers, and base layer. Thirdly, an energy attribute fusion strategy was adopted to merge the base layer, an integrated fusion strategy was adopted to merge the large-scale layers, and a max-value fusion strategy was adopted to merge the small-scale layers. Finally, the fused image was reconstructed through the sum operation of the three fused layers. Experimental results demonstrated that the proposed algorithm can effectively reduce the probability of noise generation and overcome the shortcomings of missing information in the fused image.