Predicting Camera Sensor Noise Propagation to Displacement and Strain Maps Retrieved from Checkerboard Patterns with Localized Spectrum Analysis

Abstract

Background

Reliably predicting the metrological performance of full-field measurement systems is a topical issue in the photomechanics community.

Objective

The objective of this paper is to propose predictive equations giving the pixelwise standard deviation distribution of the noise affecting displacement and strain maps retrieved from checkerboard patterns with the Localized Spectrum Analysis (LSA).

Methods

Predictive equations already available for the noise in phase distributions are employed to deduce their counterparts for the noise in displacement and strain maps. Two procedures are proposed to improve the reliability of the predictive equations. One is based on filtering the pseudo-periodic signal-dependent component of the noise, the other on the Generalized Anscombe Transform GAT, which stabilizes image noise variance, and thus leads to a better match of one of the assumptions under which the predictive equations are obtained.

Results

The predictive equations given in this paper are validated with synthetic and experimental data.

Conclusions

The predictive equations proposed here enable us to reliably predict image noise propagation to displacement and strain maps retrieved from checkerboard pattern images by LSA.