To address the blurring of imaging images caused by unfavorable factors such as CCD acquisition and imaging focus depth in optical microscopic imaging systems, this study proposes a method for enhancing the clarity of bright-field microscopic images of unsharp masks by improving the anisotropy-guided filtering. First, the image noise is eliminated by improving the anisotropy-guided filtering of the adaptive regularization term while strongly retaining the edge detail information. Next, the isotropic Gaussian filtering is used to achieve strong smooth image structure information and the details are strongly preserved. The detail-preserving image is subtracted from the strongly-smoothed image to obtain the mask image. Finally, the input image is fused with the mask image to obtain a clear microscopic image. The proposed method overcomes the phenomenon that the traditional unsharp mask algorithm amplifies the image noise and reduces the recognition of image detail features. Furthermore, our method reduces the blurring of the image to a certain extent and avoids gradient inversion and halo artifacts in the filtering results. In the experimental results of the Leishmania parasite dataset on the Mendeley data public database, the average value of the sharpness evaluation indicators such as the peak signal-to-noise ratio, root mean square error, and average structural similarity is 41.1430, 0.000090, and 0.9905, respectively. Meanwhile, in the experimental results on the PanNuKe public database, the average value of the abovementioned three indicators is 28.8541, 0.001939, and 0.9283, respectively. Subjective and objective experimental results show that the proposed method can reduce noise interference and suppress detail "halo" problems as well as enhance edge detail information.