Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint
Abstract The 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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Hindawi-IET
2021-05-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12023 |
| _version_ | 1797429308338208768 |
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| author | P. Li T.M. Laleg‐Kirati |
| author_facet | P. Li T.M. Laleg‐Kirati |
| author_sort | P. Li |
| collection | DOAJ |
| description | Abstract The 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. |
| first_indexed | 2024-03-09T09:11:17Z |
| format | Article |
| id | doaj.art-c284b61d630b468b99054211c8c8b377 |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T09:11:17Z |
| publishDate | 2021-05-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-c284b61d630b468b99054211c8c8b3772023-12-02T08:33:51ZengHindawi-IETIET Signal Processing1751-96751751-96832021-05-0115319520610.1049/sil2.12023Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraintP. Li0T.M. Laleg‐Kirati1Computer, Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal KSAComputer, Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal KSAAbstract The 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.https://doi.org/10.1049/sil2.12023electrocardiographyGaussian distributionmedical signal processingsignal denoising |
| spellingShingle | P. Li T.M. Laleg‐Kirati Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint IET Signal Processing electrocardiography Gaussian distribution medical signal processing signal denoising |
| title | Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint |
| title_full | Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint |
| title_fullStr | Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint |
| title_full_unstemmed | Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint |
| title_short | Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint |
| title_sort | signal denoising based on the schrodinger operator s eigenspectrum and a curvature constraint |
| topic | electrocardiography Gaussian distribution medical signal processing signal denoising |
| url | https://doi.org/10.1049/sil2.12023 |
| work_keys_str_mv | AT pli signaldenoisingbasedontheschrodingeroperatorseigenspectrumandacurvatureconstraint AT tmlalegkirati signaldenoisingbasedontheschrodingeroperatorseigenspectrumandacurvatureconstraint |