小波神经网络程序基于反向传播训练

用vc6.0开发出一小波滤波的程序。内容包括仿真数据生成，文本数据读取，滤波器系数构造，小波分解与重构，小波阈值及阈值函数选择，小波降噪。-vc6.0 developed using a wavelet filter procedures. Include simulation data generation, text to read data, filter coefficients structure, wavelet decomposition and reconstruction. Wavelet threshold and threshold function selection, wavelet denoising.

基于小波变换的图像压缩方法。应用SOPM改善。-Based on wavelet transform image compression method. SOPM applications to improve.

离散正交小波变换及小波重构，程序中给出测试数据，可得到这组数据小波分解后的低频分量和重构以后的数据-Discrete orthogonal wavelet transform and wavelet reconstruction, procedures given in the test data, available in this set of data after wavelet decomposition and reconstruction after low-frequency component data

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

最新的小波变换图象处理-latest wavelet transform image processing

小波变换的一些源程序-wavelet transform some source

关于小波的图像分析的源码-on wavelet analysis of the source image

一种基于模糊规则和小波变换的医学图像锐化增强算法-Based on fuzzy rules and wavelet transform for medical image sharpness enhancement algorithm

实行针对图像的DCT以及IDCT变换的源程序。-instituting image DCT and IDCT transformation of the source.

小波图象去噪综述谢杰成张大力徐文立-Wavelet Image Denoising vigorously Jie Zhang Xu Wenli