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我实现的离散小波变换，可直接运行，采用三级变换(Discrete wavelet thransform)

基于小波和插值的超分辨率图像重建算法，通过插值后将小波分解应用于图像中，再通过逆变换得到重构图像(And interpolation based on wavelet super-resolution image reconstruction algorithm by interpolating the image after the wavelet decomposition used, then the reconstructed image through the inverse transform)

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.)

基于小波变换的信号去噪技术，简单的一维小波去噪程序(Signal de-noising based on wavelet transform technique, a simple one-dimensional wavelet denoising procedure)

这篇代码介绍了一种阵列信号处理的一种算法，具有很大的作用。(This code describes an algorithm for an array signal processing, has a big role.)

常用的db，haar，墨西哥帽等小波的程序，帮助认识小波的性质(This is commonly used dB, Haar, Mexico hat and other wavelet program, can help you understand the nature of wavelet)

小波方法进行降噪，阈值函数来完成不同设计，核心程序如下面的所示，使用的小波基有coif5，db3，sym4，bior2.4，haar，rbio(Wavelet method for noise reduction, design to complete different threshold function, the core program as shown below, using the wavelet base coif5, db3, sym4, bior2.4, haar, rbio )

小波阈值去噪 软阈值去噪和硬阈值去噪的比较(Wavelet thresholding soft thresholding and hard thresholding comparison)

加速度振动信号积分为速度振动信号输出，如需要可再一次积分，变换为位移信号(Vibration signal of the acceleration integral transform)

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.)