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dc.contributor.authorLi, P.
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.date.accessioned2021-06-22T12:50:02Z
dc.date.available2019-12-24T11:05:26Z
dc.date.available2021-06-22T12:50:02Z
dc.date.issued2021-04-07
dc.date.submitted2020-12-30
dc.identifier.citationLi, P., & Laleg-Kirati, T. M. (2021). Signal denoising based on the Schrödinger operator’s eigenspectrum and a curvature constraint. IET Signal Processing, 15(3), 195–206. doi:10.1049/sil2.12023
dc.identifier.issn1751-9675
dc.identifier.issn1751-9683
dc.identifier.doi10.1049/sil2.12023
dc.identifier.urihttp://hdl.handle.net/10754/660806
dc.description.abstractThe authors propose an adaptive, general and data-driven curvature penalty for signal denoising via the Schrödinge operator. The term is derived by assuming noise to be generally Gaussian distributed, a widely applied assumption in most 1D signal denoising applications. The proposed penalty term is simple and in closed-form, and it can be adapted to different types of signals as it depends on data-driven estimation of the smoothness term. Combined with semi-classical signal analysis, we refer this method as C-SCSA in the context. Comparison with existing methods is done on pulse shaped signals. It exhibits higher signal-to-noise ratio and also preserves peaks without much distortion, especially when noise levels are high. ECG signal is also considered, in scenarios with real and non-stationary noise. Experiments validate that the proposed denoising method does indeed remove noise accurately and consistently from pulse shaped signals compared to some of the state-of-the-art methods.
dc.description.sponsorshipThe research reported here was supported by King Abdullah University of Science and Technology (KAUST) Base Research Fund, (BAS/1/1627-01-01).
dc.publisherInstitution of Engineering and Technology (IET)
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1049/sil2.12023
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleSignal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal KSA
dc.identifier.journalIET Signal Processing
dc.eprint.versionPublisher's Version/PDF
dc.identifier.volume15
dc.identifier.issue3
dc.identifier.pages195-206
dc.identifier.arxivid1908.07758
kaust.personLi, P.
kaust.personLaleg-Kirati, T.M.
kaust.grant.numberBAS/1/1627-01-01
dc.date.accepted2021-02-03
refterms.dateFOA2019-12-24T11:05:47Z
kaust.acknowledged.supportUnitBAS
kaust.acknowledged.supportUnitBase Research Fund


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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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