Assessment of speckle pattern quality in digital image correlation from the perspective of mean bias error

Abstract

Digital image correlation (DIC) technology is severely affected by the speckle pattern quality. Most of the existing assessment methods always use only one parameter to evaluate both mean bias error and standard deviation error, such as the mean intensity gradient (MIG). However, the principles of these two error models are quite different. The mean bias error is closely related to the first-order and second-order gradients of speckle pattern intensity. A parameter named Ef, based on the mean bias error, is proposed to evaluate the speckle pattern quality in this work. Numerical translations are applied to eight different speckle patterns to justify its correctness. The results indicate that the existing MIG is efficient to assess speckle pattern quality by evaluating the standard deviation error, while the parameter Ef is efficient to assess the speckle pattern quality from the perspective of mean bias error.