Multi-focus image fusion is an prominent branch of image fusion， which is widely used in microscopic imaging. Aiming at the problems of unclear texture details and misjudgment of focus areas in multi-focus fusion， this paper designs a global information encoding-decoding network from the perspective of global attention of spatial and channel information， combined with the shifted window self-attention mechanism in Swin Transformer and deep separable convolution. The comprehensive loss function is used to perform unsupervised learning of image reconstruction tasks. From the perspective of the importance of feature neighborhood information， an improved Laplacian energy sum function is introduced to discriminate the image focusing-properties in the feature domain， and the fine-grained effect of image focusing region discrimination is enhanced. Compared with seven classical image fusion algorithms， the proposed algorithm achieves advanced fusion performance in both qualitative and quantitative analysis and has a higher fidelity effect on the focus area information of the original image.