Hessian-based adaptive optimized subset DIC method for shape and deformation measurement

Digital image correlation (DIC) has been widely recognized as a powerful and non-contact optical measurement technique for full-field deformation analysis. However, robustness and computational efficiency of DIC are often degraded by poor speckle quality, illumination variation, and non-uniform speckle distribution. To address these challenges, a Hessian-based and adaptively subset allocation DIC method for deformation measurement is proposed. The multi-scale Hessian enhancement enhances speckle clarity while suppressing fringe artifacts, yielding higher-quality images for correlation analysis. Furthermore, an adaptive subset allocation strategy based on speckle density of local grayscale statistics is developed, smaller subsets in high-density areas and larger subsets in low-density regions are employed to improve the computational efficiency. Both simulated and experimentally captured speckle images are used to validate the proposed method. The experimental results demonstrate that the proposed method significantly improves measurement accuracy and computational efficiency under various interference conditions, which provides a robust and efficient solution for dynamic three-dimensional shape and deformation measurement in complex environments.