H2

对信号f(x)进行三层小波变换，得到小波系数d1,d2,d3和逼近系数a3，我想求出各个频率段的能量（能量=小波系数的平方和），很不错的应用程序(The signal f (x) for three wavelet transform, wavelet coefficients d1, d2, d3 and approximation coefficients a3, I would like to find the energy of each frequency band (energy = sum of squared wavelet coefficients), very good application)

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.)

实现了对图像最基本的分解重构融合的较为简单的程序 (Realize the decomposition of the image reconstruction of the most basic integration of relatively simple procedures)

对于非平稳信号，小波多尺度分解师一种有效的信号去噪方法，在小波去噪方法的基础上，提出了基于Lipschitz指数的小波阈值去噪方法(For non-stationary signals, signal de-noising of wavelet multi-scale decomposition ' is an effective method, wavelet denoising method based on proposed wavelet thresholding denoising method based on the Lipschitz exponent)

二进小波和非线性变换的图像增强项目 二进小波和非线性变换的图像增强项目(Image enhancement project based on two in wavelet and nonlinear transform)

AR过程的线性建模的Burg算法参数估计m文件(Burg method for AR processing)

小波去噪,通过对含噪信号进行四层小波分解，此处利用db3小波系列，通过仿真发现可以很好的消除信号中的噪声(Wavelet denoising, the signal through four layers of noisy wavelet decomposition, where the use db3 wavelet series, the simulation found in the well to eliminate the noise signal)

说明：  通过高斯函数导数检测图像边缘，实质是方向可调小波变换检测图像边缘（8个方向变换）。(Gaussian function through derivative edge detection in real terms is adjustable direction edge detection wavelet transform (change the direction of 8).)

基于小波变换的图片重建 有完整代码 工程 以及说明 涉及到图片的切割(image reconstruction based on wavelet translate )

基于小波变换的随机噪声降噪的matlab 主要运用的方法有软阈值去噪法和硬阈值去噪法(Based on wavelet transform matlab random noise and noise reduction methods are mainly used soft thresholding method and hard thresholding method)