matlabwaveletthreshod

小波基矩阵乘法快速算法工具箱，里面有目录，供大家学习参考！(Tools for Fast Matrix Multiplication in Wavelet Basis)

说明：  压缩感知代码,运用正交匹配追踪算法回复，简单易懂，很适合刚刚接触压缩感知的朋友参考。(compressed sensing code)

基于小波变换的图像去噪的matlab程序的源代码 基于小波变换的图像去噪的matlab程序的源代码(里面包含了源代码和运行的结果,(Image denoising based on wavelet transform of the matlab program s source code Image denoising based on wavelet transform of the matlab program s source code (which contains the source code and run the results of)

FMCW调频连续波雷达的测距测角，可以得到很精确的幅值、频率、相位估计，有小波分析的盲信号处理。( FMCW frequency modulated continuous wave radar range and angular measurements, You can get a very accurate amplitude, frequency, phase estimation, There Wavelet Analysis Blind Signal Processing.)

Matlab 二进小波变换程序，求取模极大值程序(Matlab wavelet transform into two procedures, to strike maxima procedures)

求冲激函数、阶跃函数及三角函数的小波变换、模极大线并求它们的李氏指数(Demand impulse function, step function and the trigonometric functions of the wavelet transform modulus maxima line and ask them Lipschitz)

整数小波变换，可以直接运行，对图像小波分解(Integer wavelet transform)

小波变换的MATLAB源程序，使用语语音增强，去除噪声效果好，算法实现简单，清晰(MATLAB source wavelet transform, using language speech enhancement, noise removal effect, algorithm is simple and clear)

小波十讲中文、切边优化版本，适合直接在kindle阅读(<Ten Lectures on Wavelets>)

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.)