Spectral denoising based on Hilbert–Huang transform combined with F-test - Bian, Xihui; Ling, Mengxuan; Chu, Yuanyuan; Liu, Peng; Tan, Xiaoyao (Frontiers in Chemistry, 2022)
Due to the influence of uncontrollable factors such as environment and instruments, noise is unavoidable in a spectral signal, which may affect the spectral resolution and analysis result. In the present work, a novel spectral denoising method is developed based on Hilbert-Huang transform (HHT) and F-test. In this approach, the original spectral signal is firstly decomposed by empirical mode decomposition (EMD). A series of intrinsic mode functions (IMFs) and a residual (r) are obtained. Then, the Hilbert transform (HT) is performed on each IMF and r to calculate their instantaneous frequencies. The mean and standard deviation of instantaneous frequencies are calculated to further illustrate the IMF frequency information. Thirdly, the F-test is used to determine the cut-off point between noise frequency components and non-noise ones. Finally, the denoising signal is reconstructed by adding the IMF components after the cut-off point. Artificially chemical noised signal, X-ray diffraction (XRD) spectrum and X-ray photoelectron spectrum (XPS) are used to validate the performance of the method in terms of signal to noise ratio (SNR). Results show that the method provides superior in denoising capabilities compared with Savitzky-Golay (SG) smoothing.