Adaptive wavelet de-noising based on FDR step-up-down procedure

The threshold de-noising method based on the wavelet transform is an effective approach to reduce the white noise of the digit signal. Considering the different characters of signal and white noise in the wavelet domain, a novel algorithm to determine the adaptive wavelet threshold is proposed with the use of False Discovery Rate (FDR) step-up-down procedure. Since the wavelet de-noising transform process can be regarded as a multiple hypothesis process and both the step-up and step-down procedures can control the FDR independently, the new method FDR step-up-down procedure combining the above-mentioned procedures determines the wavelet threshold. An attractive advantage of this method is it can obtain the desired result by adjusting the FDR level flexibly. Numerical results show that this procedure works as effective as Hearsure procedure and gives better Signal Noise Ratio (SNR) gains and Mean Square Error (MSE) performance than both the traditional BH FDR and Sqtwolog procedure. In addition, this proposed algorithm has a substantially better power and higher resolution speed.