fourier变换的原代码,原代码由C语言开发,内包含测试图片

是图像的二维小波变换加上压缩编码，效果好-Is the image together with two-dimensional wavelet transform coding, effective

ICA算法The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.-ICA algorithm:The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.

小波图像处理程序-wavelet image processing

小波算法以及提升格式的实现。用C++实现,Visual C++,Wavelet-wavelet algorithm and the realization upgrade format. C to achieve, Visual C, Wavelet

dwt小波变换用于图像处理与压缩-dwt wavelet transform for image processing and compression

小波算法的VC源代码，说明详细，有很强的参考价值-Wavelet algorithm VC source code, detailed description, have a strong reference value

这是一组二维小波变换的经典程序，对研究小波的很有用，里边有对应说明-This is a group of two-dimensional wavelet transform of the classical procedures, the study of wavelets useful, inside there is the corresponding description

静态切分窗口的曲线y=ax*x+10*x+10图，根据a在[-10，10]区间变化时，曲线随着变化-static window splitting the curve y = ax 10* x* x 10 maps, in accordance with a [-10, 10] interval changes, in keeping with the changing curve

小波变换编码 ！图象处理-Wavelet Transform Coding! Image Processing

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