Wavelet De-noising with Double-Threshold Based on Particle Swarm Optimization
Kaiyu Li · 2009
To improve the de-noising performance of filters,we present a new method to optimize the two thresholds based on particle swarm optimization(PSO) in the wavelet domain.Having analyzed the widely used soft threshold and hard threshold de-noising methods,we select the Donoho threshold as the upper threshold,and minimax threshold as the lower threshold.The PSO algorithm is used to optimize the double-threshold for the best threshold for de-noising.Simulation results show that the proposed method can overcome the psuedo-Gibbs phenomenon of the hard-thresholding method and excessive smoothness of the signal caused by soft-thresholding.It enhances SNR and reduces RMSE,providing better performance than the traditional methods.