Development of Optimal Multiscale Patterns for Digital Image Correlation via Local Grayscale Variation

In some applications of digital image correlation (DIC), adequately quantifying deformation of a material can require identification of local deformations which are much smaller than the total field of interest. Instead of exhaustively stitching together images taken at high magnification, it is more efficient to utilize multiple magnifications. Unfortunately, it is rare that the material naturally has features that are useful for image correlation at multiple magnifications. Therefore, an ideal pattern was sought that (1) contains features appropriate for the multiple magnifications, (2) need not know location of high magnification a priori, and (3) can be viewed with standard DIC equipment. An optimization framework was developed based on the inclusion of local grayscale biases which can produce multiscale DIC patterns that satisfy these criteria. Numerical and physical experiments were also performed to illustrate the functionality and utility of the designed patterns