The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images. To meet these requirements， an autoencoder-based method for infrared and visible image fusion is proposed. The encoder designed according to the optimization objective consists of a base encoder and a detail encoder， which is used to extract low-frequency and high-frequency information from the image. This extraction may lead to some information not being captured， so a compensation encoder is proposed to supplement the missing information. Multi-scale decomposition is also employed to extract image features more comprehensively. The decoder combines low-frequency， high-frequency and supplementary information to obtain multi-scale features. Subsequently， the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction. Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.