Surface data imputation with stochastic processes

Spurious measurements in surface data are common in technical surfaces. Excluding or ignoring these spurious points may lead to incorrect surface characterization if these points inherit features of the surface. Therefore, data imputation must be applied to ensure that the estimated data points at spurious measurements do not strongly deviate from the true surface and its characteristics. Traditional surface data imputation methods rely on simple assumptions and ignore existing knowledge of the surface, yielding in suboptimal estimates. In this paper, we propose using stochastic processes for data imputation. This approach, which originates from surface simulation, allows for the straightforward integration of a priori knowledge. We employ Gaussian processes for surface data imputation with artificially generated missing features. Both the surfaces and the missing features are generated artificially. Our results demonstrate that the proposed method fills the missing values and interpolates data points with better alignment to the measured surface compared to traditional approaches, particularly when surface features are missing.