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Reducing the systematic error of DIC using gradient filtering
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DOI：10.1016/j.measurement.2022.112366.png)
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Abstract
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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%.
Keywords:
DIGITAL IMAGE CORRELATION
GAUSS-NEWTON ALGORITHM
REGISTRATION ALGORITHMS
INTERPOLATION BIAS
STRAIN-MEASUREMENT
SPECKLE PATTERNS
MEAN INTENSITY
NOISE
DISPLACEMENT
REDUCTION
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5.6
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