Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

Abstract

Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

Optimized super-resolution promote accuracy for projection speckle three-dimensional digital image correlation

“…SR is an image post-processing technique that combines multiple low-resolution (LR) images containing complementary information to create a high-resolution (HR) image [25,26]. It enables the generation of HR images without decreasing the field of view or requiring smaller measurement distances [27]. SR technique aligns multiple LR images with overlapping information onto a common grid to eliminate the effects of device vibration on image shifts [28], then the overlapping information in the LR image pixels is combined into one HR pixel, enhancing the information that appears multiple times in the LR image set [29].…”

“…Hansen et al [27] proposed a method that utilizes SR technology to obtain high resolution images for DIC analysis at long working distances, demonstrating that SR technology can be effectively paired with DIC to reduce errors. Lyons et al [31] applied SR technology to infrared DIC measurements and demonstrated its ability to effectively remove noise and speckle features, resulting in a reduction of the average measurement error.…”

Stereo digital image correlation using binocular super-resolution

“…The most strategy is camera array method [8,[11][12][13], which uses multiple cameras for image or result mosaic and fusion to achieve HR. Among them, deep learning is applied to splice images from multiple cameras [13,14]. With the development of deep learning in super-resolution, there is an opportunity to improve the quality of DIC image [15].…”