Continuous image representation based on deep learning for reducing interpolation bias in DIC

• Proposed a deep learning based speckle images continuous representation method • Breaking the curse of DIC sinusoidal bias caused by traditional interpolation algorithms • The first study uses deep learning to solve the problem of DIC interpolation bias • Realized super-resolution of speckle images at any multiple After more than forty years of development, the accuracy of digital image correlation (DIC) methods has reached an extremely high level. However, the interpolation bias of DIC has not been resolved. With the flourishing of deep learning in the field of image superresolution, it has become possible to use deep learning-based image superresolution methods to reduce DIC interpolation bias. To achieve this goal, this paper improves the local implicit image function (LIIF) method based on the characteristics of speckle images to obtain LIIF-S, achieving continuous image representation and arbitrary resolution interpolation. Subsequently, LIIF-S is used as the interpolation algorithm of the inverse compositional-Gaussian Newton (IC-GN) method to reduce the interpolation bias. The simulation experiment results show that LIIF-S not only improves the accuracy by more than one order of magnitude compared to traditional interpolation algorithms but also that the interpolation bias does not have sinusoidal characteristics. In addition, the effectiveness and generalization of the LIIF-S method in unseen real-world scenarios have also been demonstrated through physical experiments. The code and dataset are publicly available at https://github.com/LianpoWang/SLIIF .