Stereo digital image correlation using binocular super-resolution

The spatial resolution and measurement accuracy of the digital image correlation (DIC) method are constrained by camera resolution. This limitation is primarily determined by hardware costs. However, in current stereo DIC measurements, only the gray level or its gradient from two images is used for integer-pixel matching and sub-pixel optimization. It implicitly treats the two images from different viewpoints as independent entities before correlating them. However, the inherent structural information has not been fully utilized. This previously overlooked structural information provides a novel approach to enhancing the accuracy of DIC by leveraging the inherent correlations between stereo image pairs. The realization of binocular super-resolution typically requires a relatively small parallax. Moreover, the DIC method can achieve image window pairing with small parallax through pre-matching. This implies that binocular super-resolution and Stereo-DIC can complement each other by sharing information. In this paper, the DIC method is employed for whole-pixel image matching, while the binocular super-resolution method, based on deep learning, is applied to process the matched image pairs. Building on previous experiments, extensive datasets containing diverse experimental scenes and various speckle patterns were compiled and utilized. Furthermore, the DIC method can establish training datasets with minimal parallax through integer-pixel matching, thereby achieving highly effective super-resolution results. Experimental results demonstrate that super-resolution images with a higher signal-to-noise ratio can be obtained. Additionally, it effectively provides more image details, which enhance the calculation accuracy and resolution of DIC.