  Smoothing is widely used approach for measurement noise reduction in spectral
analysis. However, it suffers from signal distortion caused by peak
suppression. A locally self-adjustive smoothing method is developed that
retains sharp peaks and less distort signals. The proposed method uses only one
parameter that determines global smoothness, while balancing the local
smoothness using data itself. Simulation and real experiments in comparison
with existing convolution-based smoothing methods indicate both qualitatively
and quantitatively improved noise reduction performance in practical scenarios.
We also discuss parameter selection and demonstrate an application for the
automated smoothing and detection of a given number of peaks from noisy
measurement data.
