The measurement of microscopic vision is commonly used in micro-assembly and other fields. However， due to limitations such as depth of field in microscopic imaging， the image may appear blurred and affect the accuracy of measurement. Although the technology of auto-focusing in optical microscopy can alleviate defocusing problems， it will be too time-consuming to adapt to the requirements of efficient production. Herein， an end-to-end deblurring model that integrates blurring discrimination and multi-branch recovery was presented， in which a divide-and-conquer strategy of chunking， discrimination， deblurring， and fusion was established. Firstly， the image was divided into sub-images， which were then simultaneously processed by a discriminator and a recovery network. The discriminator employed the Fourier transform to obtain the frequency-domain map of the sub-images. From the frequency domain map， the Vision Transformer network extracted deep blur features with global correlation. The output of the blurring degree was then discriminated. The multi-branch recovery network was used to directionally recover sub-images with different blurring degrees based on the discriminative output. Finally， the spliced sub-images were fused to obtain high-resolution images. The experimental results indicate that the model can effectively restore multi-blurred microscopic images， with a discriminator accuracy reaching 0.94. Moreover， after undergoing processing by the multi-branch restoration network， the PSNR metric shows an average improvement of 6.3.