A Monte Carlo approach to quantifying model error in Bayesian parameter estimation

The slanted edge method is a basic approach for measuring the modulation transfer function (MTF) of imaging systems; however, its measurement accuracy is limited in practice. Theoretical analysis of the slanted edge MTF measurement method performed in this paper reveals that inappropriate edge angles and random noise reduce this accuracy. The error caused by edge angles is analyzed using sampling and reconstruction theory. Furthermore, an error model combining noise and edge angles is proposed. We verify the analyses and model with respect to (i) the edge angle, (ii) a statistical analysis of the measurement error, (iii) the full width at half-maximum of a point spread function, and (iv) the error model. The experimental results verify the theoretical findings. This research can be referential for applications of the slanted edge MTF measurement method.

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A Monte Carlo approach to quantifying model error in Bayesian parameter estimation

Cited by 2 publications

References 46 publications

A Monte Carlo approach to quantifying discrepancies between intractable posterior distributions

A Monte Carlo approach to quantifying discrepancies between intractable posterior distributions

Error of the slanted edge method for measuring the modulation transfer function of imaging systems

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