Enhancing Digital Image Correlation with Adaptive Wavelets: Addressing Complex Deformations and Lorenz Noise

Abstract

This paper introduces an Adaptive Wavelet-Based Correlation (AWC) model to enhance Digital Image Correlation (DIC) in complex noise and deformation conditions. Traditional DIC methods, such as Zero-mean Normalized Cross-Correlation (ZNCC), often struggle with robustness in high-noise environments and intricate deformation patterns. The AWC model leverages wavelet transforms to improve local image analysis, offering increased accuracy and stability. Extensive validation demonstrates that AWC outperforms ZNCC in both precision and computational efficiency. In particular, the model proves valuable for structural health monitoring, material testing, and high-strain experimental mechanics, where accurate deformation tracking under noisy conditions is critical. The proposed methodology is implemented in MATLAB, and the full code is available for replication and further research.