MATLAB2

离散小波变换与离散小波反变换 快速小波变换（Mallat小波分解算法）：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）(Discrete wavelet transform and discrete wavelet inverse transform fast wavelet transform (Mallat wavelet decomposition): For an image to do 2 wavelet transform (DWT) and synthesis (discrete wavelet inverse transform))

小波包分解包括重构信号问题程序，这是一个matlab程序，经过测试可用，数据采用自己的数据。(Including the reconstructed signal wavelet packet decomposition problem program, which is a matlab program, tested available data using their own data.)

个人编写的提升格式97小波去噪，采用硬阈值法，三层分解，包括小波变换，去噪，逆变换三步(Written format of 97 individuals to enhance the wavelet denoising using hard threshold method, three decomposition, including wavelet transforms, denoising, inverse transform three-step)

利用小波变换，通过设置阈值对图像进行去噪。 首先对图像用小波进行2层分解，然后通过设置硬阈值和软阈值的方法，对图像高频部分去噪。(The wavelet transform is used to denoise the image by setting threshold. Firstly, the image is decomposed into 2 layers by wavelet, and then the high frequency part of the image is denoised by setting the hard threshold and the soft threshold.)

根据小波变换后的模极大值 重建原始信号(To reconstruct the original signal based on wavelet transform modulus maxima)

先进行提升小波变换，然后使用提升小波进行图像的分解和重构(Be carried out on lifting wavelet transform, and then use the images to enhance the wavelet decomposition and reconstruction)

小波变换对电能质量暂态信号的分析和运用，包含电压骤升、骤降等(Wavelet transform for power quality analysis and the use of transient signals, including voltage dips, swells, etc.)

一维小波变换的MATLAB实现方法，包括一维连续小波变换和离散小波变换。(MATLAB in a one-dimensional wavelet transform methods, including the one-dimensional continuous wavelet transform and discrete wavelet transform.)

说明：  小波(Wavelet)这一术语，顾名思义，“小波”就是小的波形。所谓“小”是指它具有衰减性；而称之为“波”则是指它的波动性，其振幅正负相间的震荡形式。与Fourier变换相比，小波变换是时间(空间)频率的局部化分析，它通过伸缩平移运算对信号(函数)逐步进行多尺度细化，最终达到高频处时间细分，低频处频率细分，能自动适应时频信号分析的要求，从而可聚焦到信号的任意细节，解决了Fourier变换的困难问题，成为继Fourier变换以来在科学方法上的重大突破。有人把小波变换称为“数学显微镜”。 这套讲义生动详实的快速带领初学者掌握小波的基本要点，是不可多得的好材料！(This is a handbook about Wavelet.)

一种新型分数阶小波变换及其应用,讨论了新型分数阶小波变换的应用(A new Fractional Wavelet Transform and Its Applications, discussed the application of the new fractional wavelet transform)