Multi-focus image fusion (MFIF) explores the positioning and reorganization of the focused parts from the input images. Focused and defocused parts have similar representations in color, contour and other appearance information, which degrades the fusion quality due to the influence of these redundant information. Currently, most MFIF methods have not identified an effective way to remove redundant information before fusion stage. Thus, in this paper, we introduce a structural map extraction strategy for multi-focus image fusion. Compared to the source image, structural map reduces redundant information, and the clearer parts of the image retain more abundant structural features. Consequently, the differences between focused part and defocused part become more pronounced based on the extracted structural map. Specifically, the proposed fusion method adopts a two-stage training strategy. Firstly, the structural map is extracted by the proposed structural map extraction network (SMENet) from the source images. Secondly, the structural map is thus applied to train the decision map generation network (DMGNet) to obtain the decision map which is utilized to generate the final fusion image. Qualitative and quantitative experiments on three public datasets demonstrate the superiority of the proposed method, compared with the advanced image fusion algorithms.