Refinement of spectra using a deep neural network: Fully automated removal of noise and background
Medhanie Tesfay Gebrekidan, Christian Knipfer, Andreas Braeuer
Abstract
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.
Topics & Concepts
Raman spectroscopySimilarity (geometry)Noise (video)Spectral lineArtificial neural networkArtificial intelligencePattern recognition (psychology)Biological systemMetric (unit)Computer scienceFeature (linguistics)Analytical Chemistry (journal)ChemistryPhysicsOpticsBiologyEngineeringChromatographyPhilosophyOperations managementAstronomyImage (mathematics)LinguisticsSpectroscopy Techniques in Biomedical and Chemical ResearchSpectroscopy and Chemometric AnalysesRemote-Sensing Image Classification