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Effective noise in thresholded intensity distribution: influence on centroid statistics.

J Ares, J Arines

|December 7, 2007

Summary

Preprocessing data with thresholding algorithms impacts optical measurement results. This study models thresholding

Area of Science:

Optical measurement
Data preprocessing
Signal processing

Background:

Centroid determination is crucial for optical measurement devices.
Centroid calculation often uses thresholded data.
The effect of preprocessing on results is understudied.

Purpose of the Study:

To analyze the influence of thresholding algorithms on data properties.
To model the impact of preprocessing on centroid determination.
To investigate additive Gaussian noise effects.

Main Methods:

Theoretical analysis of effective noise.
Simulated analysis of effective noise.
Experimental validation of the noise model.

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Main Results:

Thresholding algorithms perturb data, creating an effective additive signal perturbation.
A model for effective noise was developed and validated.
Good agreement was found between theoretical, simulated, and experimental results.

Conclusions:

Preprocessing, specifically thresholding, significantly affects statistical properties of noisy data.
The developed model accurately describes noise perturbation during preprocessing.
Findings are directly applicable to improving centroid determination accuracy.