由于成像方式及波谱接收段的不同.合成孔径雷达SAR(synthetic aperture radar)与可见光图像所反映的信息有很大差异，图像之间相关性弱，且互...

dwt小波变换，用于对图像处理与压缩-dwt wavelet transform, for the image processing and compression

二维小波变换源程序-wavelet transform two-dimensional source

一种对图像进行离散余弦变换处理的源代码-images of a pair of discrete cosine transform the source code

小波零数编码的源程序，对于学习小波用于图像编码的同学有一定帮助-Wavelet zerotree coding source for the study of wavelet image coding for the students to have a certain extent, help

这一个尝试编写的有关神经网络的训练，测试的相关VC++的源程序-try this one prepared by the neural network training and testing of the source code of VC

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

Stanford 小波变换函数集，包含很多最新的小波变换函数-Stanford wavelet transform function set, including many of the latest wavelet transform function

多尺度分析的图象处理技术，是目前比较新的一种处理方法。-multiscale analysis of image processing technology, is a relatively new approach.

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

自适应反离散小波变换，能达到很好的小波变化结果-Adaptive inverse discrete wavelet transform, wavelet transform can achieve very good results