Wavelet Transform-Based Signal Denoising in Low-Field NMR
Low-field NMR often uses permanent magnet. The signals obtained contain high level of white Gaussian noises, and have low signal-to-noise ratio (SNR). In recent years, many denoising methods have been proposed for low-field NMR measurements. Most of these methods can remove noises without losing use...
| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Science Press
2018-09-01
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| Series: | Chinese Journal of Magnetic Resonance |
| Subjects: | |
| Online Access: | http://121.43.60.238/bpxzz/EN/10.11938/cjmr20182615# |
| Summary: | Low-field NMR often uses permanent magnet. The signals obtained contain high level of white Gaussian noises, and have low signal-to-noise ratio (SNR). In recent years, many denoising methods have been proposed for low-field NMR measurements. Most of these methods can remove noises without losing useful information contained in the original signals. Wavelet transform is the most popular denoising method among them. In this paper, we first introduced the theory of wavelet transform analysis, followed by review of three wavelet transform denoising methods for low-field NMR, namely the wavelet threshold method, the wavelet transform modulus maximum method and the correlation of wavelet coefficient method. Finally, we showed that four parameters could be calculated to evaluate the denoising performance. |
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| ISSN: | 1000-4556 1000-4556 |