A LLS operator based S‐I WT de‐noising algorithm applied in EDXRF
Fei Li, Chuanfeng Tang, Hui Li, Liangquan Ge · X-Ray Spectrometry · 2020
An improved shift‐invariant wavelet (S‐I WT) de‐noising algorithm based on LLS operator is proposed for high‐resolution energy dispersive X‐ray fluorescence. Sym8 is chosen as the wavelet basis function and performed noise reduction on the analog signal. Comparison of the de‐noising effect of S‐I WT, improved WT and LLS S‐I WT (where LLS is the log square root operator) method are quantitatively evaluated by using evaluation criteria signal‐to‐noise‐ratio (SNR), root mean square error and Pearson correlation coefficient. Meanwhile, a new evaluation criterion of de‐noising effect, called peak area relative difference, is also proposed to evaluate the counting deviation. The results show that the LLS‐SI WT is simple and reliable, can effectively reduce pseudo‐Gibbs artificial signals and statistical fluctuation. Besides, this method simplifies the calculation, reduces the running time and improves the running efficiency. The LLS‐SI WT is also applied to reduce the noise after adding strong noise to the signal, the SNR has been improved from 14.0040 to 14.7552, and most of the characteristic peak information retains to the greatest extent.