Reducing the systematic error of DIC using gradient filtering. (15th February 2023)

• Record Type: Journal Article
• Authors: Cui, Hengrui; Zeng, Zhoumo; Zhang, Hui; Yang, Fenglong
• Is Part Of: Measurement. Volume 207(2023)
• Publication Date: 2023-02-15
• Subjects: Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.112366
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A gradient operator processing method is proposed, which decomposes the gradient operator into two parts: gradient information acquisition (one-dimensional gradient operator) and gradient filtering (filtering operator), facilitating the analysis of the effect of the gradient operator on the error of DIC. Using the decomposed model to analyse the effect of different filter operators on the error, the filter operator is not limited to the requirement that it needs to be perpendicular and equal in size to the gradient operator when constructing a 2-D gradient operator. The gradient filter DIC (GF-DIC) is constructed based on the results of the above analysis. The filtering process of the gradient information is added to the DIC calculation process, and a suitable filtering method is selected through the above analysis to reduce the error of the DIC calculation results. Abstract: The inverse compositional Gauss-Newton (IC-GN) DIC algorithm is now the most popular DIC algorithm. The error analysis of the algorithm is necessary. However, the effect of the gradient operator on the error cannot be systematically analysed due to the different dimensions of the gradient operator. In this paper, the 1-D and 2-D gradient operators are incorporated into the same framework by decomposing the gradient operator into two parts: gradient acquisition and gradient filtering. Based on the above analysis, a DIC method based on gradient filtering is constructed and the simulation analysis results show that the systematic error is reduced to less than 10% of the original ICGN-DIC algorithm, enhancing the robustness to noise variations. Finally, validation is performed using an open-source dataset. It is demonstrated that the proposed method can reduce the system error to less than 15%.