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Refinement of spectra using a deep neural network: Fully automated removal of noise and background
Author(s) -
Gebrekidan Medhanie Tesfay,
Knipfer Christian,
Braeuer Andreas Siegfried
Publication year - 2021
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.6053
Subject(s) - raman spectroscopy , similarity (geometry) , noise (video) , spectral line , artificial neural network , artificial intelligence , pattern recognition (psychology) , biological system , metric (unit) , computer science , feature (linguistics) , analytical chemistry (journal) , chemistry , physics , optics , biology , engineering , chromatography , linguistics , operations management , philosophy , astronomy , image (mathematics)
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.