Working Paper Article Version 1 This version is not peer-reviewed

Denoising of Spectra by Adaptive Multiwindow Model Fitting.

Version 1 : Received: 22 August 2021 / Approved: 23 August 2021 / Online: 23 August 2021 (12:28:24 CEST)

How to cite: Charonov, S. Denoising of Spectra by Adaptive Multiwindow Model Fitting.. Preprints 2021, 2021080436 Charonov, S. Denoising of Spectra by Adaptive Multiwindow Model Fitting.. Preprints 2021, 2021080436

Abstract

A method for noise reduction of spectra based on fitting a multi-window model is presented. The spectrum is modeled as the sum of the polynomial background and Lorentzian peaks. This model applies to all points in the spectrum and to all window sizes. An iterative algorithm is used for fitting. Based on the initial data, the background calculated by the direct least squares method is subtracted. Positive data values ​​are inverted using the 1/x function and the same procedure is used to fit the Lorentzian peaks. The weighted sum of all windows fit containing the point to be processed is used as the result. The weighting factors are calculated by evaluating the quality of the fit. The performance of the presented method is compared with the Savitsky-Golay method and the wavelet noise reduction method. The proposed approach provides good noise reduction performance without using user-entered parameters.

Supplementary and Associated Material

http://www.geologie-lyon.fr/Raman/: Handbook of Raman Spectra for geology
https://spectralmultiplatform.blogspot.com/p/math3denoiser.html: Online version of an adaptive multiwindow model filter

Keywords

Denoising; Savitzky-Golay filter; polynomial fitting.

Subject

MATHEMATICS & COMPUTER SCIENCE, Applied Mathematics

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