In the process of microscopic imaging, the system's depth-of-field limitation results in considerable differences in the focused positions across various planes along the axial direction. This leads to partial overlap of focused regions among microscopic images from different planes. Current multifocus fusion algorithms often struggle to simultaneously extract and merge the sharpest focused parts from multiple microscopic images. Hence, this article proposes a multifocus microscopic image fusion algorithm. First, a Gaussian-like four-neighborhood gradient operator was constructed and combined with fast guided filtering to extract high-frequency focus information. Additionally, a small region focus measurement method was introduced to enhance the extraction for high-frequency focus information from sharply focused regions considering the overlap of focus information and the substantial number of pixels in wide-field microscopic image sequences. This method effectively fuses the best focus points from multiple images. Through experiments, three sets of microscopic multifocus image sequences covering the diagonal fields-of-view of 4 mm and 2 mm were captured and testing was conducted. Through comparative analysis against five commonly used multifocus image fusion algorithms, our algorithm yields an average improvement in the peak signal-to-noise ratio of 2.4772 and surpasses a structural similarity index of 0.9400. These results exhibit superior fusion effects obtained by the proposed algorithm in the focused regions, rendering fused images enriched in details and high clarity. This algorithm meets the accuracy requirements for multifocus image fusion in applications involving large field-of-view microscopic images.