Quality Assessment Of Speckle Patterns In Digital Image Correlation

Digital image correlation (DIC) is a non-contact and non-interference full-field optical metrology, which is widely used in shape, motion, and deformation measurement in experimental mechanics community and receives growing attention for its simplicity and practicality. This technique has been thoroughly developed and many powerful and user-friendly commercial DIC systems are available in the market.The DIC method is based on the texture of the specimen; thus, the quality of the speckle patterns have a critical influence on the measurement performance of DIC. However, there is no unified standard for the produce of speckle patterns. Different scholars use different speckle patterns; different company recommend different speckle patterns; for practitioners, it is difficult to choose which pattern to use, and it is even difficult to justify which pattern is better. This phenomenon is due to the lack of valid assessment of speckle patterns. The assessment of speckle patterns demands thoroughly knowledge of errors in DIC for the optimized speckle patterns has the smallest errors. Nevertheless, after a decade’s study, the inherent nature of interpolation bias, a sys-tematic error of DIC, is still unexplained; significant difficulties occur in the study of interpolation bias; there is no method to estimate the interpolation bias efficiently; in-terpolation bias have been the bottleneck problem of speckle assessment. Besides, for noise-induced bias, which is a systematic error of DIC as well, existing methods fail to estimate the noise-induced bias for high accuracy generalized interpolation methods such as B-spline,O-MOMS.The aim of this dissertation is to solve the problem of interpolation bias, present a formula of noise-induced bias for generalized interpolation, and present a valid speckle pattern assessment. The major achievements of this dissertation are as follows. (1) In the context of interpolation bias, analytical formulae of interpolation bias for both con-tinuous and discrete signals are derived; a concept called interpolation bias kernel is presented, and it is shown that the interpolation bias is determined by the integral of the product of interpolation bias kernel and image power spectrum; a simple, fast, and effective method to estimate the interpolation bias is proposed. The stochastic integral method is introduced to DIC community and a significant decrease of the interpolation bias is achieved. (2) In the context of noise-induced bias, a more general, briefer, and more elegant theoretical framework for noise-induced bias is presented, a simple yet effective method is proposed to estimate the noise-induced bias, and the cause of noise-induced bias is explained intuitively. (3) In the context of random errors, a formula of random errors under non-uniform noise is derived and verified. (4) In the context of speckle patterns assessment, a formula of total errors is derived; valid speckle assess-ment parameters are presented by considering the interpolation bias, the noise-induced bias, and the random errors in total.