Fast and large-converge-radius inverse compositional Levenberg–Marquardt algorithm for digital image correlation: principle, validation, and open-source toolbox

Abstract

This paper presents an inverse compositional Levenberg-Marquardt (IC-LM) algorithm for robust, efficient, and accurate image registration in digital image correlation (DIC). In essence, the IC-LM algorithm is a mixture of the classical inverse compositional Gaussian-Newton (IC-GN) and gradient descent algorithms. Further normalization of the local coordinate and image intensity is also introduced to adaptively initialize the damping parameter in the IC-LM algorithm. The proposed IC-LM algorithm is proven to hold a larger converge radius while having comparable accuracy, precision, and efficiency compared with the classical IC-GN algorithm. The efficient reliability-guided displacement tracking strategy is also merged into the IC-LM algorithm to provide an accurate initial guess for all calculation points. For the sake of reproducibility of this algorithm, the open-source MATLAB toolbox featuring the IC-LM algorithm is available on GitHub (https://github.com/cbbuaa/DIC_ICLM_MATLAB).