In order to further improve the contrast and sharpness of the fused image, a multi-focus image fusion algorithm based on the non-subsampled shearlet transform (NSST) and guided filtering is proposed in this paper. Firstly, multi-scale and multi-directional decomposition of multi-focus source images are performed by using NSST transform. Then, the low-frequency sub-band coefficients are used to construct the initial fusion weights by calculating the local region Sum-Modified-Laplacian energy sum. The initial fusion weights are corrected by the guided filter. A weighted fusion rule based on local region Sum-Modified-Laplacian energy sum and guided filtering is proposed. For the high-frequency sub-band coefficients, combined with human visual characteristics, the initial fusion weights are constructed by the significant information, the local region average gradient, edge information, and local region Sum-Modified-Laplacian energy sum; the initial fusion weights are modified according to guided filtering, and a guided filtering weighted fusion rule based on human visual characteristics is proposed. Finally, the inverse NSST is used to produce the fused image. The simulation results of the four groups of multi-focus source images demonstrate that, not matter it is the subjective evaluation or the objective evaluation, the proposed algorithm not only preserves the details such as edge contour, and texture of the source images, but also improves the contrast and clarity of the fused image.