With a test signal, this paper introduced three methods on threshold denoising: force threshold, default threshold and given threshold.
结合某测试信号，介绍了阈值消噪的三种方法：强制阈值、默认阈值和给定阈值。
And then, establish the adaptive thresholding algorithm. Thus, we get a new adaptive threshold denoising method fit for coherent noise.
并在此基础上确定了自适应阈值计算方法，建立了新的阈值去噪法，可用于去除相干噪声。
Combined with the characteristics of seismic image noise, using multi-scale wavelet transform, presents a new adaptive wavelet threshold denoising algorithm.
结合地震图像噪声的特点，利用多尺度小波变换的优点，提出一种新的自适应小波阈值去噪算法。
Aiming at the disadvantages of traditional soft and hard threshold methods for wavelet threshold denoising, the new developed extracting threshold denoising method is proposed.
针对小波阈值去噪方法中传统软硬阈值法各自存在的缺点，提出了开方法阈值去噪法。
The experiment shows that image denoising using the robust local threshold performs better than that using the universal threshold.
用均方差衡量去噪性能，实验表明用鲁棒局部阈值去噪法好于全局阈值去噪法。
In traditional denoising methods for signals, we usually use the method of threshold to achieve denoising by the threshold disposing of wavelet packet transform.
传统信号去噪方法常采用门限法对噪声信号的小波或小波包变换做阈值处理以达到去噪的目的。
Aimed at the ultrasonic signal polluted by noises, which is low in SNR, a method of the ultrasonic signal denoising in wavelet domain based on an improved threshold function was put forward.
针对超声无损检测中超声信号受噪声污染、信噪比不高的情况，提出了一种新的基于改进阈值函数的小波域超声信号去噪方法。
This article proposed a method to mark denoising threshold from study function of wavelet nerve network in order to improve performance of denoising to signals.
针对某一确定数据采集系统中小波去噪时的阈值选择，提出以小波神经网络加标准信号来标定去噪阈值的方法，从而提高对信号的去噪性能。
Experimental results prove that the select of coherent ratio threshold is independent of image types or noise level. So the way of denoising presented here is adaptive.
实验证明了相干比阈值的选择与图像的类型和噪声水平没有关系，因而本文所提出的图像去噪方法是自适应的。
Compared with the traditional threshold functions, the improved one was more advantageous in ultrasonic signal denoising.
相对于经典的阈值函数，改进的阈值函数在超声信号去噪中更具有优势。
The result verifies that the new method possesses both merits of hard and soft threshold methods, offers better denoising effect and higher signal to noise ratio.
结果证明，新方法兼顾了软硬阈值法的优点，去噪效果更好，信噪比更高。
This paper introduces the application of wavelet transformation in speech signal denoising, describes in detail the method of soft threshold and hard threshold and points out their disadvantages.
研究了基于小波变换的语音去噪问题，在对传统阈值法去噪和语音特性分析基础上，提出一种改进的多阈值法语音信号去噪方法。
By Analysing speech signal and conventional denoising method of threshold, a new mult-threshold based on wavelet transform method of speech signal denoising is proposed.
研究了基于小波变换的语音去噪问题，在对传统阈值法去噪和语音特性分析基础上，提出一种改进的多阈值法语音信号去噪方法。
Combining with the denoising principle, the rational selection issue concerning more suitable wavelet base and the best threshold rule in actual application is discussed and compared.
基于小波变换对信号去噪进行了深入地分析和研究，结合去噪原理讨论和比较了实际应用中对小波基及阈值规则的合理选取问题。
Combining with the denoising principle, the rational selection issue concerning more suitable wavelet base and the best threshold rule in actual application is discussed and compared.
基于小波变换对信号去噪进行了深入地分析和研究，结合去噪原理讨论和比较了实际应用中对小波基及阈值规则的合理选取问题。
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