subtraction-speech-denoising

小波包和EMD方法对信号分析后的频谱图比较，可以对EMD的运行参数进行修改。(Spectrum comparison of Wavelet package and EMD)

小波谱分析mallat算法经典程序 希望对大家的学习有帮助(The small spectral analysis of the classical program of the mallat algorithms hope that everyone' s learning)

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.)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing

用于进行小波变换，对原始的光谱图像数据进行预处理(Used wavelet transform, the original spectral image data preprocessing)

用matlab实现的短时傅里叶变换和小波变换。有文档(Using matlab to achieve short-time Fourier transform and wavelet transform. A document)

嵌入式小波图像编码即EZW是最近很流行的一种编码方式，我的程序是用matlab实现对EZW的描述。(Embedded Wavelet EZW image coding that is very popular recently as a way of coding, I use the matlab process is to achieve a description of the EZW.)

应用Matlab进行小波包对时变谐波分析的代码(Matlab Applications for wavelet packet on time-varying harmonic analysis)

说明：  图像小波变换，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。(Image Wavelet Transform)

离散小波分解，把图像分为高频细节与低频概貌。(Discrete wavelet decomposition, the image is divided into high and low frequency detail overview)