Signal denoising based on the Schrödinger operator's eigenspectrum and a curvature constraint
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal KSA
KAUST Grant NumberBAS/1/1627-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/660806
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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.
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
SponsorsThe research reported here was supported by King Abdullah University of Science and Technology (KAUST) Base Research Fund, (BAS/1/1627-01-01).
JournalIET Signal Processing
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