wavedec2

小波变换(wavelet transform，WT)是一种新的变换分析方法，它继承和发展了短时傅立叶变换局部化的思想，同时又克服了窗口大小不随频率变化等缺点，能够提供一个随频率改变的"时间-频率"窗口，是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征，能对时间(空间)频率的局部化分析，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。(Wavelet transform (WT) is a new transform analysis method. It inherits and develops the idea of localization of short-time Fourier transform and overcomes the shortcomings that window size does not vary with frequency. It can provide a frequency-dependent The Time-Frequency window is ideal for time-frequency analysis and processing of signals. Its main feature is that by transforming some features that can fully highlight some aspects of the problem, the localization of the time (space) frequency can be analyzed, and the signal (function) can be gradually and multi-scale refined by the scaling operation, finally reaching the high frequency Time subdivision, frequency subdivision at low frequencies, can automatically adapt to the requirements of time-frequency signal analysis, which can focus on any details of the signal to solve the difficulties of Fourier transform has become a major breakthrough in the scientific method since Fourier transform.)

说明：  基于二进小波变换的图像小波模极大值检测，以及交替投影法重建(Based on dyadic wavelet transform modulus maxima of wavelet image detection, as well as the alternating projection reconstruction)

对信号进行局部均值分解，得到一系列具有物理意义的pf分量(Local mean decomposition of the signal to obtain a series of having a physical meaning PF component)

生成高斯波形脉冲，并求解高斯脉冲的自相关结果(Gaussian pulse waveform is generated, and solve the results of Gaussian pulse autocorrelation)

波的合成及拍频现象实例，输入两正弦波的振幅、初相位及频率观察波的合成及拍频现象(Synthesis and beat frequency wave phenomena instance, enter the two sine wave amplitude, initial phase and frequency of observation of waves synthesis and beat frequency phenomenon)

用于图像的多尺度分析，shapelet变换用matlab 编程实现(shapelet code)

可用于曲波变换的工具箱，用于含噪声的图像的去噪过程,图像分解，有例子可直接运行。只可用于学术研究目的。(the Latest curvelet transform lab tool ，Non-commercial research use for Academics. Image decomposition, there are examples can be run directly.)

dual tree complex wavelet transform

小波图像去噪源码，很不错，内有程序的运行结果(Image denoising source, very good, there are programs running results)

基于小波变换的图像压缩编码，实现了多种小波变换，并且实现了12:1的高压缩率。(Based on Wavelet Transform Image Coding, to achieve a variety of wavelet transform, and achieved a high 12:1 compression ratio.)