An appropriate thresholding method of wavelet denoising for dropping ambient noise

Abstract

For the non-stationary signal denoising, an effective method for dropping ambient noise is based on discrete wavelet transform. Also, in order to minimize the loss of useful signal and get high SNR in the wavelet denoising, it is very important that the thresholding is suitable for the characteristics of signal. In this paper, we propose new thresholding method to reduce an ambient noise and to detect effectively the useful signal. First, we analyze four kinds of previous wavelet threshold functions (Hard, Soft, Garrote and Hyperbola) and propose new wavelet threshold function compromised between Garrote and Hyperbola threshold functions. Next, a threshold value is selected by value to reduce exponentially according to the wavelet decomposition level. We also analyze a continuity and monotonicity, and prove the logic of new threshold function. The results of theoretical analysis show that new threshold function solves the problems of constant error and discontinuity of previous threshold functions, and minimizes the information loss of useful signal. The results of experiment show that SNR of new thresholding method is highest and RMSE and Entropy are smallest. The results of theoretical analysis and experiment show that new thresholding method is more appropriate to wavelet denoising for dropping ambient noise than previous methods.