TWO WAVELET THRESHOLD DE-NOISING METHODS BASED ON LOCAL VARIANCES

It is dissatisfactory to implement wavelet threshold to de-noise only with wavelet coefficient magnitudes. In this paper two novel methods of wavelet threshold de-noising based on locale variances are presented. They use the wavelet coefficient magnitudes as well as the local variances, which denote the clustering properties of wavelet coefficients preferably. Some simulation results, obtained from recovering a signal embedded in the additive white noise in different signal-to-noise (SNR) settings, show the potential and effectiveness of the proposed methods.