An application of wavelet transform toward noisy NMR peak suppression

Abstract

A shift averaged Harr wavelet transform is introduced as a new and excellent tool to distinguish real peaks from the noise contaminated NMR signal. An application of discrete wavelet transform (DWT) to the noise suppression in time domain NMR data has been successfully accomplished. A simple DWT with a set of Daubechies wavelet coefficients (1/2, -1/2), Harr wavelet in combination with shift averaging of NMR data was applied to the noise elimination. New algorithms of shift averaged Harr wavelet were quantitatively evaluated in terms of threshold and signal to noise ratio (SNR), and were utilized to suppress several noisy NMR spectra for application.