Optimal determination of wavelet threshold and decomposition level via heuristic learning for noise reduction

For the research field of adaptive de-noisy, the technique which determinates an adequate threshold in wavelet domain of signal is novel and feasible. The most of threshold determination are developed from universal method proposed by Donoho. Unfortunately, these methods are just performed in some wavelet level and involve several incorrectly estimated factors; therefore, they can't result the best performance and can't work well in general cases. By the reason, this paper replaces the universal threshold determination by an intelligent determination, genetic algorithm (GA). Because original signals and noise are mutually independent, an objective function of GA is created to evaluate the second order correlation and high order correlation. Moreover, GA searching is applied in progressively wavelet levels to explore the most suitable wavelet decomposition and the optimal threshold. In order to confirm the validity and efficiency of the proposed algorithm, several simulations which include four benchmarks with variant noise degrees are designed. Moreover, the performance of proposed algorithm will have compared with that of other existing algorithms.