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Recently, a new Signal processing method, named Semi-Classical SignalAnalysis (SCSA), has been proposed for denoising Magnetic ResonanceSpectroscopy (MRS) signals. It is based on the Schr\"odinger Operator'seigenspectrum. It allows an efficient noise reduction while preserving MRSsignal's peaks. In this paper, we propose to extend this approach to differentsignals, in particular pulse shaped signals, by including an optimization thatconsiders curvature constraints. The performance of the method is measured byanalyzing noisy signal data and comparing with other denoising methods. Resultsindicate that the proposed method not only produces good denoising performancebut also guarantees the peaks are well preserved in the denoising process.

Signal denoising based on the Schr\"odinger operator's eigenspectrum and a curvature constraint