Uneven intensity change correction of speckle images using morphological Top-Hat transform in digital image correlation

Gu, Gao; Wang, Y.

doi:10.1179/1743131x15y.0000000026

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…Some studies have reported that light variation has a great influence on the accuracy and stability of identification results for DIC method [ 31 ]. In order to exploit the influence of light condition on the accuracy of the proposed method, the light conditions were set as light and dark groups, and their grayscale image is shown in Figure 13 .…”

Section: Experimental Verificationmentioning

confidence: 99%

Subpixel Matching Using Double-Precision Gradient-Based Method for Digital Image Correlation

Liu

Zhang

et al. 2021

Sensors

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Digital image correlation (DIC) for displacement and strain measurement has flourished in recent years. There are integer pixel and subpixel matching steps to extract displacement from a series of images in the DIC approach, and identification accuracy mainly depends on the latter step. A subpixel displacement matching method, named the double-precision gradient-based algorithm (DPG), is proposed in this study. After, the integer pixel displacement is identified using the coarse-fine search algorithm. In order to improve the accuracy and anti-noise capability in the subpixel extraction step, the traditional gradient-based method is used to analyze the data on the speckle patterns using the computer, and the influence of noise is considered. These two nearest integer pixels in one direction are both utilized as an interpolation center. Then, two subpixel displacements are extracted by the five-point bicubic spline interpolation algorithm using these two interpolation centers. A novel combination coefficient considering contaminated noises is presented to merge these two subpixel displacements to obtain the final identification displacement. Results from a simulated speckle pattern and a painted beam bending test show that the accuracy of the proposed method can be improved by four times that of the traditional gradient-based method that reaches the same high accuracy as the Newton–Raphson method. The accuracy of the proposed method efficiently reaches at 92.67%, higher than the Newton-Raphon method, and it has better anti-noise performance and stability.

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Section: Experimental Verificationmentioning

confidence: 99%

Subpixel Matching Using Double-Precision Gradient-Based Method for Digital Image Correlation

Liu

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Several studies dealt with brightness corrections, since in practice it is not always feasible to maintain the same conditions for every acquired image throughout experiments. Peng et al [20] assumed linear changes, Gu et al [21] used a top-hat correction for tackling uneven intensity changes, and Hu et al [22] proposed a position-based intensity change model. The so-called zero-mean normalization of images is another common choice [1,23,24,25].…”

Section: Introductionmentioning

confidence: 99%