Blind super-resolution algorithms based on diffusion models still face significant challenges at the current stage, including high computational cost, long inference time, and limited cross domain generalization ability. This paper aims to apply super-resolution algorithms to the field of optical microscopy imaging to reveal more microscopic structures and details. Firstly, we proposed a lightweight super-resolution model called ResShift-4E, which is an optimized model from two important aspects: reducing the diffusion steps in ResShift and strengthening the influence of the original residuals on model learning. Secondly, we constructed a dataset of Multimodal High-resolution Microscopy Images (MHMI) including a total of 1220 images, which is available on line. Moreover, we extended our model to application-oriented research on blind image super-resolution of optical microscopy imaging. The experimental results demonstrate that our ResShift-4E model outperforms other models on various microscopy images.