Cell microscopic imaging is an important tool for cell phenotype detection. Traditional fluorescence imaging techniques are widely used in the cell imaging. However, the fluorescence imaging instruments have complex structure and high cost. Besides, staining could cause damage to cells. To address this problem, this paper proposes a virtual staining technique that performs strict alignment of bright field and fluorescence image datasets using a multimodal alignment algorithm, and improves network architecture, loss function, post-processing, and hardware adaptation for training optimization. The staining conversion bias is calculated by the evaluation criteria of the virtual staining. The method presented in this paper could simplify the fluorescence imaging equipment and eliminates the need for various staining operations, which could reduce the burden of research and diagnostic processes for biologists and pathologists.