Bias error reduction of digital image correlation using Gaussian pre-filtering

• The effect of noise on DIC measurements is investigated in this work.
• Gaussian pre-filtering of speckle images is proposed for bias error reduction in DIC.
• Numerical and real experiments verify the effectiveness of the proposed technique.

In digital image correlation (DIC), the iterative spatial domain cross-correlation algorithm using high-order B-spline interpolation algorithms has been strongly recommended for accurate sub-pixel displacement measurement. However, the magnitude of the position-dependent bias error increases with the increase of noise level, which dramatically reduces the registration accuracy of DIC for real experimental images. In this paper, a simple method, based on pre-smoothing the speckle images with a 5×5 pixels Gaussian low-pass filter prior to correlation analysis, is proposed for reducing the bias error in measured displacements. Both numerical simulations and real experiments reveal that the proposed technique is capable of reducing the bias error in measured displacement to a negligible degree for both noisy and noiseless images, even though a simple bicubic interpolation is used.