Abstract
The wavelet transform de-noising method based on the threshold shrinkage is widely used, however, the classical threshold shrinkage method maybe exists constant deviation or appears additional concussion after reconfiguration. If the threshold value is chosen too large, part of useful signal points will be eliminated; if chosen too small, part of the noise will be kept. In addition, the “first generation wavelet” de-noising method only can choose one type of wavelet base, and can't be changed during the de-noising process. Due to the different wavelet has its own characteristics and applicable signal, It can achieve local optimum then overall optimization that choosing wavelet in a small wave focus according to the partial feature of the signal, namely using the multiwavelets to comprehensively decompose signal. This paper puts forward a new threshold function, then improves the selection scheme of threshold value, achieves that it can select dynamically according to the wavelet decomposition levels; Finally, on the basis of the lifting theory, this paper puts forward an adaptive method, which can select wavelet dynamically according to the partial feature of the signal, comprehensively making use of the advantages of each wavelet, to achieve better de-noising effect and a certain degree of adaptability. Experiments show the effectiveness of our optimization.
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