Previous studies mainly concentrated on the wavelet transform domain.
以前的研究主要是集中在小波变换域来进行的。
参考来源 - 基于变换域的数字水印方法研究The wavelet transform in denoising has been achieved some good results.
目前,小波变换技术在去噪方面取得了良好的效果。
参考来源 - 语音编码中的小波去噪算法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
This article utilizes the wavelet transform to compress the high dimensional face image vectors, then devises an SVM classify system to recognition the face.
文中使用小波变换来对人脸的高维图像矢量进行压缩，并设计了一个支持向量机分类器系统来识别人脸。
The wavelet transform is of good localization property in the domain of time and frequency, which provides a comprehensive application in processing acoustic wave.
小波变换同时有在时间域和频率域对信号进行局部化的特点，使其在声波信号处理中有着广泛的应用前景。
The continuous and discrete wavelet transform are also described. The constraints to wavelet, qraphics and general properties about the wavelet transform are also presented.
描述了连续和离散的子波变换，讨论了子波函数成立的容许性条件和子波变换的一些基本性质。
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