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Refinement of spectra using a deep neural network: Fully automated removal of noise and background

Standard

Refinement of spectra using a deep neural network: Fully automated removal of noise and background. / Gebrekidan, Medhanie Tesfay; Knipfer, Christian; Braeuer, Andreas Siegfried.

in: J RAMAN SPECTROSC, Jahrgang 52, Nr. 3, 09.03.2021, S. 723-736.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Gebrekidan, MT, Knipfer, C & Braeuer, AS 2021, 'Refinement of spectra using a deep neural network: Fully automated removal of noise and background', J RAMAN SPECTROSC, Jg. 52, Nr. 3, S. 723-736. https://doi.org/10.1002/jrs.6053

APA

Gebrekidan, M. T., Knipfer, C., & Braeuer, A. S. (2021). Refinement of spectra using a deep neural network: Fully automated removal of noise and background. J RAMAN SPECTROSC, 52(3), 723-736. https://doi.org/10.1002/jrs.6053

Vancouver

Gebrekidan MT, Knipfer C, Braeuer AS. Refinement of spectra using a deep neural network: Fully automated removal of noise and background. J RAMAN SPECTROSC. 2021 Mär 9;52(3):723-736. https://doi.org/10.1002/jrs.6053

Bibtex

@article{66faa9e89a1c4abd9eb9878b563b9129,
title = "Refinement of spectra using a deep neural network: Fully automated removal of noise and background",
abstract = "We report the potential of U-Net deep neural network for the efficient removal of noise and background from raw Raman spectra. The U-Net method was first trained on simulated spectra and then tested with experimental spectra. The quality of the test results was quantified via different signal-to-noise ratios and the structural similarity index metric. The U-Net recovered Raman spectra feature a high structural similarity index, even for raw spectra that were dominated by background. The U-Net model does not rely on any human intervention.",
keywords = "deep learning, fluorescence rejection, noise reduction, Raman spectra, U-Net",
author = "Gebrekidan, {Medhanie Tesfay} and Christian Knipfer and Braeuer, {Andreas Siegfried}",
note = "Funding Information: The project leading to this result has received funding from the European Union's Horizon 2020 research and innovation program under ERC Starting Grant agreement 637654 (Inhomogeneities). It has also received funding from the Wilhelm Sander‐Stiftung, Munich, Germany (Grants 2017.111.1 and 2017.111.2). Publisher Copyright: {\textcopyright} 2021 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons Ltd. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = mar,
day = "9",
doi = "10.1002/jrs.6053",
language = "English",
volume = "52",
pages = "723--736",
journal = "J RAMAN SPECTROSC",
issn = "0377-0486",
publisher = "John Wiley and Sons Ltd",
number = "3",

}

RIS

TY - JOUR

T1 - Refinement of spectra using a deep neural network: Fully automated removal of noise and background

AU - Gebrekidan, Medhanie Tesfay

AU - Knipfer, Christian

AU - Braeuer, Andreas Siegfried

N1 - Funding Information: The project leading to this result has received funding from the European Union's Horizon 2020 research and innovation program under ERC Starting Grant agreement 637654 (Inhomogeneities). It has also received funding from the Wilhelm Sander‐Stiftung, Munich, Germany (Grants 2017.111.1 and 2017.111.2). Publisher Copyright: © 2021 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons Ltd. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/3/9

Y1 - 2021/3/9

N2 - We report the potential of U-Net deep neural network for the efficient removal of noise and background from raw Raman spectra. The U-Net method was first trained on simulated spectra and then tested with experimental spectra. The quality of the test results was quantified via different signal-to-noise ratios and the structural similarity index metric. The U-Net recovered Raman spectra feature a high structural similarity index, even for raw spectra that were dominated by background. The U-Net model does not rely on any human intervention.

AB - We report the potential of U-Net deep neural network for the efficient removal of noise and background from raw Raman spectra. The U-Net method was first trained on simulated spectra and then tested with experimental spectra. The quality of the test results was quantified via different signal-to-noise ratios and the structural similarity index metric. The U-Net recovered Raman spectra feature a high structural similarity index, even for raw spectra that were dominated by background. The U-Net model does not rely on any human intervention.

KW - deep learning

KW - fluorescence rejection

KW - noise reduction

KW - Raman spectra

KW - U-Net

U2 - 10.1002/jrs.6053

DO - 10.1002/jrs.6053

M3 - SCORING: Journal article

AN - SCOPUS:85099266182

VL - 52

SP - 723

EP - 736

JO - J RAMAN SPECTROSC

JF - J RAMAN SPECTROSC

SN - 0377-0486

IS - 3

ER -