基于小波修剪阈值法的消噪外文翻译全文.doc

外文翻译（原文） -1- -2- WAVELET DE-NOISING BY MEANS OF TRIMMED THRESHOLDING Abstract: Wavelet thresholding de-noising techniques provide a new way to reduce noise in signal. However, the soft thresholding is best in reducing noise but worst in preservingedges, and hard thresholding is best in preserving edges but worst in de-noising. Motivated by finding a more general case that incorporates the soft and hard thresholding to achieve a compromise between the two methods, the trimmed thresholding method is proposed in this paper. Finally, the experiment results and the power spectral analysis show that the trimmed thresholding is superior to hard and soft thresholding methods. I. INTRODUCTION De-noising is a permanent topic for engineers and applied scientists. In recent years, wavelet de-noising has been more and more extensive in signal processing. As a new signal processing method,wavelet analysis has characteristics of multi-resolution and multi-scale.It can make us observe the signal progressively from coarse to fine and have the ability to perform local signal characteristics in both time domain and frequency domain. Wavelet transform de-noising is an important aspect to make wavelet analysis applied in engineering practice. In principle, any arithmetic that can use the Fourier transform can use wavelet transform,and it is not limited by short-time window.So it is widely used in signal processing. There are many ways of wavelet de-noising, the more influential, and most commonly used two methods are wavelet transform modulus maxima method of noise reduction and nonlinear wavelet threshold de-noising method. In signal processing, noise reduction with a small crossing analysis has been more widely used, It has been successfully applied in many fields. Such as the seismic signal noise reduction, remote sensing image noise reduction, speech signal noise reduction, noise reduction of nonlinear time series. II. PRINCIPLE OF WAVELET MULTIRESOLUTION Wavelet de-noising is b