DWT.m

说明：  借助小波变换在时频分析上的优点，提取地面运动目标的回波信号特征在目标识别中加以运用(With wavelet transform in time-frequency analysis of the advantages of extracting ground moving target echo signal characteristics to be used in target recognition)

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)

实现文章 李根.基于小波变换修正的双线性图像插值图像放大方法，信息技术，2010.9 一文的仿真。包括自编的双线性插值和最近邻插值方法。(Lee, realize the article based on wavelet transform image modified bilinear interpolation method of image magnification, information technology, article 2010.9 of the simulation. Including self bilinear interpolation and nearest neighbor interpolation method.)

自己编写离散小波变换与离散小波反变换程序对一幅图像做2级小波分解（离散小波变换）与合成（离散小波反变换）(Write your own discrete wavelet transform and discrete wavelet inverse transform procedure on an image to do two wavelet transform (DWT) and synthesis (discrete wavelet inverse transform))

用MATLAB编写的利用小波变换对多聚焦图像的融合。(The fusion of multi focus images with wavelet transform is written in MATLAB.)

matlab计算haar 小波各级近似系数和细节系数的频率响应（CA1，CD1，CA2，CD2，CA3），并画出幅频图，便于理解小波变换的意义，(U191 u7197 u5c1 u5c1 u5Fc1 u4Fc u653 u564C u548C u7286 u8282 u7CFB u6570 u7684 u9891 u54CD u5E94 uFF08CA1 uFF0CCD1 uFF0CCA2 uFF0CCD2 UFF0CCA3 uFF09 uFF0C u5E76 u753B u51FA u5E45 u9891 u56FE uFF0C u4FBF u4E8E u7406 u89E3 u5C0F u6CE2 u53D8 u6362 u7684 u610F u4E49 uFF0C)

小波变换 求模极大值 三次B样条小波变换 希望对大家有帮助 (Wavelet transform modulus maxima cubic B-spline wavelet transform and we hope to)

本代码给出了图像的二维离散小波变换分解、重构图像以及误差图像。(The code gives the two-dimensional image decomposition of discrete wavelet transform, image reconstruction and the error image.)

小波变换程序，用于对一维信号，二维信号进行稀疏变换(wavelet transform)

循环平稳信号是一类特殊的非平稳信号，其统计特征具有随时间呈周期变化的特点。旋转机械由于周期运行方式其振动信号具有循环平稳特性，因此利用循环平稳分析方法能够提取出在平稳假设下所不能得到的隐藏故障特征信息，为有效地分离和识别旋转机械早期微弱故障特征提供可能。(The cyclostationary signal is a special type of non-stationary signal whose statistical characteristics have a periodic variation with time. Due to the cyclical operation of the rotating machinery, the vibration signal has the characteristics of cyclostationary stability. Therefore, the cyclostationary analysis method can extract the hidden fault feature information that can not be obtained under the stationary assumption, which provides the possibility to effectively separate and identify the early weak fault features of the rotating machinery.)