Wavelet Threshold Ultrasound Echo Signal Denoising Algorithm Based on CEEMDAN
Zhiwei Li, Huyue Xu, Bibo Jiang, Fangfang Han
Abstract
In this study, an algorithm for denoising ultrasound echo signals in industrial settings is proposed to address the problem of high noise and low signal-to-noise ratio. The algorithm combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), mutual information entropy (MIE), and wavelet threshold denoising to ensure effectiveness given the unique structure of ultrasound echo signals. Initially, CEEMDAN is used to decompose the signal into intrinsic mode function (IMFs) and residual signals. The MIE is then used to determine the correlation of neighboring IMF signals, which are then divided into a noise- and a signal-dominated part. Finally, using wavelet thresholding, noise is suppressed in the signal-dominant part, and the resulting denoised signal is reconstructed using the residual signal. The performance of the algorithm is verified through simulations and physical experiments, and the results show that it is superior to traditional signal denoising methods.