A Photometric approach to Digital Image Correlation with a Super-Resolved digital twin (SR-PhDIC)

• an interpolation-free unbiased DIC method is proposed. • one general photometric functional is considered that compares a rendering model with all available real images. • an efficient alternating optimization algorithm is proposed. • the method yields both a super-resolved estimation of the digital twin (a mix between the region of interest reflectance properties, lighting conditions and sensor characteristics) and the measured displacements fields. Digital Image Correlation typically involves deforming a pixelated image in order to compare its grey levels with those of another image. To achieve sub-pixel accuracy, grey-level interpolation is required. However, this interpolation is non-physical and introduces biases that become particularly detrimental under finite strains. In this work, we propose an alternative photometric approach that entirely avoids interpolation, grounded in a rigorous formulation of the direct image formation problem. The inverse problem is then posed as the joint estimation of a super-resolved digital twin—representing the scene and sensor characteristics—and the displacement fields. Both are estimated by minimising a single cost function that compares all available real images to their synthetic counterparts generated through a physically based rendering model. This minimisation is performed using an efficient alternating minimisation scheme. Several two-dimensional test cases are analysed, demonstrating that the proposed method is effectively unbiased and exhibits significantly lower uncertainties than state-of-the-art DIC techniques.