Image noise will seriously affect the measurement accuracy of the digital volume correlation (DVC) method. Therefore, this paper studies the effect of image noise and Gaussian prefiltering on the DVC displacements measurement results through numerical simulation translation experiments, real rescan and compression experiments. The experimental results show that, Gaussian prefiltering can significantly reduce the noise level of the images, and thus mitigates the noise-induced bias error, but has little effect on the interpolation error. The reason is that the Gaussian prefiltering will reduce the gray gradient of the image. Therefore, Gaussian prefiltering can significantly improve the accuracy of the classic forward additive Newton-Raphson algorithm. But it has little effect on the accuracy of the inverse compositional Gauss-Newton algorithm. Further, the Gaussian prefiltering is used to reduce image noise, which can improve the correlation coefficient of each calculation point in the 2 algorithms, and obtain a sub-volume with a higher degree of matching.