An accurate and robust strain field smoothing method based on polynomial fitting and anisotropic diffusion in digital image correlation

作者：Wang, Zitong; Zhu, Pan^*; Liu, Lu; Zhou, Xinglin

来源：MEASUREMENT, 2024, 225: 113947.

DOI：10.1016/j.measurement.2023.113947

• Digital image correlation
• Strain field
• Anisotropic diffusion filter
• Random noise

Digital Image Correlation (DIC) is a popular optical measurement method. However, the accuracy of strain values obtained by DIC may be adversely affected by random noise. This paper proposes an Anisotropic Nonlinear Diffusion Filtering (ADF) method, combining Pointwise Least Square (PLS) with the Perona-Malik (PM) model, extracts accurate and smooth strain fields from noisy displacement fields. The strain field is extracted point by point using PLS and then smoothed by the PM model. The ADF method was tested on simulated and real speckle deformation experiments. The results indicate that the ADF method significantly reduces the negative impact of inappropriate calculation parameters, such as the strain window size, compared to the PLS method. Furthermore, the PM model was found to be better suited for smoothing strain fields compared to other image denoising models. The ADF method demonstrates excellent noise suppression performance and accurate strain identification in both simulated and real experiments.