Application of Clustering Filter for Noise and Outlier Suppression in Optical Measurement of Structured Surfaces

Abstract

In comparison to tactile sensors, optical techniques can provide a fast, non-destructive profile/areal surface measurement solution. Nonetheless, high measurement noise, unmeasured points and outliers, are often observed in optical measurement, particularly for structured surfaces. To alleviate their detrimental impacts on the characterization of surface topography as well as the examination of micro/nanoscale geometries, a post-processing filtering technique, i.e. the clustering filter, which is essentially an iterative process to find the aggregation center of a cluster of points, is implemented. The clustering filter is particularly useful for noises and outlier suppression for optical measurement of structured surfaces due to its edge-preserving capability. Five surface samples with structured features are measured by an in-house developed dispersive interferometer and a commercial white light interferometer, thereafter the measured surface data is filtered by the clustering filter. Both noise and outliers are suppressed, which not only facilitates the visualization and characterization of surface topography, but also enables the accurate evaluation of local functional geometries.

The Wavelet Transform for Feature Extraction and Surface Roughness Evaluation after Micromachining

“…They have both evolved from filtration of 2D signal collected from surface profiles, i.e., from Gaussian filter according to ISO 16610-21 [18]. Apart from Gaussian filtration, other methods are used [53][54][55][56] to detect and remove high-frequency components of so-called noise. Surface [54] and adaptive filtering [53] has become particularly popular.…”

Precision metrology: from bulk optics towards metasurface optics

“…This rapid acquisition capability enables SDPI to work in environments with significant mechanical noise and operate on production lines for the dynamic surface inspection of a moving samples. The miniaturised SDPI system has a compact size (160x120x50 mm 3 ) and has been applied to identifying defects on a microscale thin film [29] in a roll-to-roll production platform.…”

Deep learning-based boundary effect filtering for nanometric roughness of diamond-turned micro-structured surface

“…Filters usually separate the filtering base and roughness from the primary surface depending on its corresponding bandwidth [9]. Nevertheless, traditional filters when filtering micro-structured surfaces all produce boundary effect and the surface height value at the boundary area is abnormal, for example, the Gaussian filter has averaging effects and large distortions in the boundary area [11], the spline filter has the difficulty in preserving the sharp geometry of the microstructured surfaces [12], and the wavelet filter is able to extract multi-scale surface components from the primary surface but it also produces the boundary effect [13]. Guo et al [14] applied '95-99 rule' which statistically removes 5% and 1% data points for the calculations of different parameters, and the result shows that the effect of boundary effect for the evaluation of surface roughness is not negligible.…”