Noise Reduction for Low-field Pulsed NMR Signal Via Stationary Wavelet Transform

The measure technology of low-field pulsed NMR (nuclear magnetic resonance) is a new technology, which has great potential development. It can be widely used in industry and agriculture for product detection as a lossless method. The most important work of NMR measurement is the multi-exponential inversion of NMR signal based on the peak information. But the signal is interfused by noise during the measurement. It is important to reduce the noise while keeping the peaks well. The effective wavelet shrinkage method based on SWT (Stationary Wavelet Transform) is firstly introduced in this paper. All wavelet coefficients at the same scale are shrinked with the same threshold in the traditional algorithm, which is not appropriate for NMR signal because the SNR of NMR signal varies in different position. In view of this, the adaptive thresholds are introduced for NMR signal denoising. Different coefficients at the same wavelet scale are shrinked with different thresholds which are self-adaptive to SNR in our algorithm. The novel method is applied to the low-field pulsed NMR experimental equipment developed by ourselves. The experimental results indicate that our method can keep the peaks and edges while restraining noise well.