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Objective Affected by some factors, such as light, electromagnetic interference and so on, there is a certain degree of noise in the cell image sequence obtained by microscopic cell imaging system, inevitably. To get clear cell images by denoising, it is the most important for the subsequent cell morphological feature extraction and analysis steps. To eliminate the noise of cell image sequence, we introduce the time domain information of cell image sequence to build a Spatio-temporal curvature regularization constraint.Methods First, introducing the correlation of space domain and time domain in cell image sequence, we built an image sequence denoising model based on spatio-temporal curvature regularization. Then, the optimization solution of this model was solved through the augmented Lagrangian method. Finally, in order to verify the denoising effect, the denoising experiments were performed on Fibroblast and pluripotent stem cell image sequence adding with white Gaussian noise. Results Compared with total variational denoising, 3 D threshold shear denoising and spatial curvature regularization denoising, this method was superior to other three methods on the visual effect of denoising, peak signal to noise ratio (PSNR) and structural similarity (SSIM) . Conclusions Compared with three other denoising method based Spatiotemporal volume, this method makes full use of thetemporal information of cell image sequence, and can maintain the image contrast and has a better adaptability and stability with respect to Gaussian noise, and then the ringing effect is not obvious. This method can be applied to the cell morphological change detection in early processing stage.