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Finite Sampling Corrected 3D Noise with Confidence Intervals

version (19.4 KB) by David Haefner
Analysis of imager noise through the use of 3D Noise model


Updated 31 Jul 2020

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The 3D noise model is a white noise approximation consisting of 7 independent contributions that span the 3D correlation space (1 one dimensional uncorrelated processes, 3 two dimensional uncorrelated processes, and one three dimensional uncorrelated process). These scripts allow the user to simulate a noise cube, calculate the 7 independent variances, and calculate the confidence intervals. An additional script provides examples of how the functions can be called under different circumstances, for example correcting a past measurement, or iteratively calculating the noise cube by chunking the data.
This code is in support of a journal article "Finite Sampling Corrected 3D Noise with Confidence Intervals"

Cite As

David Haefner (2020). Finite Sampling Corrected 3D Noise with Confidence Intervals (, MATLAB Central File Exchange. Retrieved .

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Added support for trend removal, see IR noise evaluation for examples

Corrected file organization in zip file

% 05-19-2015 DH; Modified 0 defective case to allow large V and H input
% 05-19-2015 DH; Modified CI to calculate without stats toolbox

MATLAB Release Compatibility
Created with R2013a
Compatible with any release
Platform Compatibility
Windows macOS Linux

Inspired: Noise3d_Spatial, IR Noise Evaluation