基于matlab的gabor滤波器，选择目标频段，一个不错的信号处理工具...

小波十讲电子书（中文版，李建平，杨万年译），很实用，很难找的哦！-E-book Ten Lectures on Wavelets (Chinese version, Li Jianping, Yang Wannian translation), it is practical, it is difficult to find the Oh!

采用5/3小波提升算法编写的一维和二维信号小波分解与重构程序。给出了图象分解的演示示例。-using 5/3 wavelet algorithm to enhance the one-and two-dimensional wavelet decomposition and reconstruction process. Given the decomposition of images demonstration examples.

小波滤波的MALLAT算法,程序编写的很容易懂,能实现bd1-db10的小波分解和重构,比我前一次发的小波算法更简单易懂-Wavelet filtering MALLAT algorithm, programming is very easy to understand, to achieve bd1-db10 wavelet decomposition and reconstruction, than I made the previous wavelet algorithm is more straightforward

％ File: Jakes_filter.m ％ Software given here is to accompany the textbook: W.H. Tranter, ％ K.S. Shanmugan, T.S. Rappaport, and K.S. Kosbar, Principles of ％ Communication Systems Simulation with Wireless Applications, ％ Prentice Hall PTR, 2004.- File: Jakes_filter.m Software given here is to accompany the textbook: WH Tranter, KS Shanmugan, TS Rappaport, and KS Kosbar, Principles of Communication Systems Simulation with Wireless Applications, Prentice Hall PTR, 2004.

Visual C++小波变换技术与工程实践（小波变换现实渐进传输的应用）-Visual C wavelet transform technology and engineering practice (wavelet transform reality gradual transfer application)

its watermarking algo using dwt technique

VC实现的小波算法-VC wavelet algorithm

快速提升小波变换的源程序，可以处理一维和二维信号和图像。-quickly upgrade wavelet transform of the source, can handle one-dimensional and two-dimensional signals and images.

dwt小波变换，用于对图像处理，压缩-dwt wavelet transform, is used to image processing, compression

本书的目的主要是向读者展示傅里叶分析和小波的许多基础知识以及在信号分析方面的应用。全书分为8章和2个附录，前言部分是学习第1章至第7章的准备知识，即内积空间；第1章讲解傅里叶系列的基础知识；第2章讲解傅里叶变换；第3章介绍离散傅里叶变换以及快速傅里叶变换；第4章至第7章讨论小波，重点在于正交小波的构建；附录部分则介绍稍微复杂的一些技术主题以及演示概念或产生图形的MATLAB代码。许多关于小波的文章和参考书籍均要求读者具有复杂的数学背景知识，本书则只要求学生具有较好的微积分以及线性代数知识，通俗易懂，是数学、计算机、电子、通信、地质、医学、机械等专业高年级本科生及研究生的基础教科书，也可作为相关技术人员的参考书。-The main purpose of this book is to show readers Fourier and wavelet analysis of many of the basic knowledge as well as in signal analysis applications. The topics are divided into eight chapters and two appendices, is to learn from the preamble of Chapter 1 to Chapter 7 of the preparation of knowledge, that is, inner product space Chapter 1 on the basis of Fourier series of knowledge Chapter 2 on Fourier Transform Chapter 3 introduced the Discrete Fourier Transform and Fast Fourier Transform Chapter 4 to Chapter 7 to discuss wavelet, with emphasis on construction of orthogonal wavelet appendix describes slightly complex themes, as well as a number of technical concepts or pr