Image-Based Crack Detection Using Total Variation Strain DVC Regularization

Introduction: Accurately detecting cracks is crucial for assessing the health of materials. Manual detection methods are time-consuming, leading to development automatic techniques based on image processing and machine learning. These utilize morphological material deformation analysis through Digital Image or Volume Correlation (DIC/DVC) identify cracks. The strain field derived from DIC/DVC tends be noisy. Traditional denoising sacrifice spatial resolution, limiting their effectiveness in capturing abrupt structural deformations such as fractures. Method: In this study, a novel DVC regularization method proposed obtain sharper less noisy field. minimizes total variation components assumption approximate constancy within phases. Results: methodology validated using simulated data actual 4D μ-CT experimental data. Compared classical methods, provides more reliable crack with fewer false positives. Conclusions: results highlight possibility estimating low-noise without relying smoothness assumption, thereby improving accuracy reliability detection.