Article
Version 1
This version is not peer-reviewed
Estimation of Gaussian Noise in Spectra by the Selective Polynomial Fit
Version 1
: Received: 2 August 2021 / Approved: 3 August 2021 / Online: 3 August 2021 (16:00:16 CEST)
Version 2 : Received: 5 August 2021 / Approved: 5 August 2021 / Online: 5 August 2021 (15:24:18 CEST)
Version 2 : Received: 5 August 2021 / Approved: 5 August 2021 / Online: 5 August 2021 (15:24:18 CEST)
How to cite: Serguei, C. Estimation of Gaussian Noise in Spectra by the Selective Polynomial Fit. Preprints 2021, 2021080098 Serguei, C. Estimation of Gaussian Noise in Spectra by the Selective Polynomial Fit. Preprints 2021, 2021080098
Abstract
This article describes an algorithm for estimation the variance of Gaussian noise. The data is smoothed using the Savitsky-Golay polynomial filter. Absolute differences between original and smoothed data are sorted in ascending order. The initial part of this sequence is selected for analysis. The result of calculation mean value of differences can be used to estimate the variance of the noise. By selecting points for analysis, the impact of cosmic ray noise and other artifacts can be reduced. The use of the proposed method for artificial and real spectra shows the ability to effectively estimate the noise variance. The algorithm contains no user-defined parameters.
Supplementary and Associated Material
http://www.geologie-lyon.fr/Raman/: Handbook of Raman Spectra for geology
https://doi.org/10.6084/m9.figshare.14991567: Data sets
https://spectralmultiplatform.blogspot.com/p/mathsmooth.html: The online noise calculator
Keywords
Gaussian noise; variance estimation.
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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