An Improved Algorithm for Peak Detection Based on Weighted Continuous Wavelet Transform

Peak detection is a crucial preprocessing step in the analysis of various spectral signals. The method based on the continuous wavelet transform is more practical and popular, and has better detection accuracy and reliability by identifying peaks across scales in the wavelet space and implicitly removing noise as well as the baseline. However, there are inevitably overlapping peaks in the measured spectra, and the formed composite ridges affect peak detection accuracy. Most peak detection methods have limited applicability to overlapping peaks. A weighted continuous wavelet transform (WCWT) peak detection algorithm is proposed to improve the adaptive ability of the peak detection method. This method yields more obvious spectral peak characteristics in low-scale regions. Composite ridges can be successfully truncated by setting a noise threshold based on the standard deviation of the spectral signal. In addition, the maximum value in the ridges was compressed and shifted to a smaller scale, which could determine the peaks more accurately. The method was applied to the peak detection of simulated spectra, Romanian database of Raman spectra, and real liquid electrode glow discharge spectra. The results show that the proposed method exhibits good peak detection performance.