Improved accuracy in laser triangulation by variance-stabilizing transformations

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Abstract
The definition of laser triangulation imaging is above all restricted by speckle noise corrupting the signals of the widely used CCD-line or matrix sensors. Speckle noise can be reduced by certain optical means or signal processing. In the field of signal processing for laser triangulation, a lot of different interpolation approaches are in common use. Regarding laser triangulation imaging as a stochastic process the variance of the corrupted data can be stabilized using principal component analysis. Subsequent nonlinear regressions yield a remarkable improvement in accuracy. The approach can be recommended if the data set is consistent and the required additional number crunching power can be provided in real time.
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