scope

小波变换模极大值重建信号的matlab源程序，来源于胡广书的《现代信号处理教程》附属光盘(Wavelet transform modulus maxima reconstruction signal matlab source code, derived from HU-book " modern signal processing tutorial subsidiary Disc)

Wavelet compression algorithm. Start by computing a complete wavelet-transform using the cohen-daubechies-fevereau (2,2) biorthogonal wavelet (which incidentally seems to be the same as used for lossless JPEG2000, but they call it a (5,3)?).

基于gabor小波变换的论文，对做人脸识别很有用(Gabor wavelet transform-based papers, do face recognition is useful)

数字信号处理C语言程序集。里面包含全部的数字信号处理中各种算法的C程序设计。(Digital Signal Processing C-assemblies. Which contains all of the digital signal processing algorithms in a variety of C programming.)

53小波变化的vc程序，很详细，有一维和二维，可以对图像进行处理。(53 wavelet transform vc program, very detailed, one-dimensional and two-dimensional image processing.)

这是小波包程序，好不容易从网上下下的，希望对大家有用！可直接使用。 (Wavelet packet, and finally from the Internet under the hope that useful! Can be used directly.)

希尔伯特变换法提取零相位子波的小程序，很好用！！(Hilbert transform method to extract the zero-phase wavelet procedure, easy to use! !)

小波阈值法去噪的程序，其中有关于信噪比和均方根误差的公式(wavelet denoising)

数字信号处理的c++库，很好的资源，作者很牛，包括数值计算与常用的信号处理分析,例如FFT、小波变换、功率谱估计等(Digital Signal Processing c++ library, a great resource,including numerical analysis and common signal processing，such as FFT, wavelet transform, power spectrum estimation)

在理解了离散小波变换的基本原理和算法的基础上，通过设计VC程序对简单的一维信 号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波和Haar小波变换，从而得到小波分解系数；再通过改变分解得到的各层高频系数进行信号的小波重构达到消噪的目的。在这一程序实现的过程中能直观地理解信号小波分解重构的过程和在信号消噪中的重要作用，以及在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。(In understanding the basis of discrete wavelet transform basic principles and algorithms, through the design VC program Daubechies wavelet, Morlet wavelet and Haar wavelet transform after a simple one-dimensional signal plus a Gaussian white noise, resulting wavelet coefficients wavelet reconstruction signal again by changing the layers to achieve high-frequency coefficients decomposed noise cancellation purposes. Can intuitively understand wavelet decomposition and reconstruction process and an important role in signal de-noising in the process of implementation of this program, and in the high-frequency coefficients when the weight of the layers selected for signal processing coefficients denoising affected.)