Digital image correlation (DIC) is a widely used non-contact technique for full-field displacement measurement. Data-driven deep-learning DIC offers rapid inference but may generalize poorly to unseen speckle patterns, deformation modes, noise levels, and illumination conditions. Classical optimization-based DIC and recent image-constrained neural formulations avoid labeled displacement data, yet their convergence and accuracy remain vulnerable to initialization, large rigid-body motion, fine non-uniform deformation, and gray-value disturbances. This paper presents a global-to-local hybrid neural DIC framework for robust pixelwise measurement. Stage 1 estimates a dominant in-plane rigid transformation through coarse rotation search, phase correlation, and masked refinement, providing a reliable initialization under large translation and rotation. Stage 2 represents the residual displacement with a hybrid Kolmogorov-Arnold network (KAN) and multilayer perceptron (MLP), and optimizes it using a valid-overlap, illumination-robust structural loss. The local gray-value model explicitly accommodates multiplicative and additive intensity changes. Simulated and real experiments demonstrate accurate and stable displacement recovery under noise, non-uniform deformation, large rotation, illumination variation, and degraded imaging conditions.

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