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.　©　2007　IEEE.