Abstract
A typical challenge with subset based DIC algorithms is to find the optimal subset size for the problem. Small subsets may improve spatial resolution, but also increase noise. Larger subsets offer better noise rejection at the cost of resolved features. In general, the product of spatial resolution and noise is approximately constant. Therefore, changing the subset size offers limited options to achieve good spatial resolution and smooth data. To resolve this dilemma, an anisotropic denoising technique is presented which utilizes a DIC uncertainty estimator as an input. The anisotropic denoising will smooth the vector field only where local gradients are smaller than the uncertainty and preserve regions with strong gradients. We will give several examples of the performance of the anisotropic denoising, including DIC challenge sample 14.
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© 2017 The Society for Experimental Mechanics, Inc.
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Grewer, M., Wieneke, B. (2017). DIC Anisotropic Denoising Based on Uncertainty. In: Yoshida, S., Lamberti, L., Sciammarella, C. (eds) Advancement of Optical Methods in Experimental Mechanics, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-41600-7_14
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DOI: https://doi.org/10.1007/978-3-319-41600-7_14
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