Wavelet Thresholding Denoising Based On Simplex-Simulated Annealing Algorithm

Expounding the basic theory and method of removing noises from signals with wavelet analysis, the determination of the threshold has an impact on the quality of removing noises from signals. This paper presents an new method based on generalized cross validation (GCV), it can get optimal thresholds of every wavelet subband by using the method of simplex-simulated annealing algorithm without requiring the prior knowledge of the noise variance, at the same time this method is independent of the choice of initial threshold, and it not only gets the global optimum, but also enhances searching efficiency. And then using Matlab to realize simulation of wavelet denoising by programming, the results show that the threshold in this paper is excellent compared with four threshold selection rules (Rigrsure, Sqtwolog, Heursure, Minimaxi), and it gives better SNR gains and RMSE performance of denoising effects.