小波分析

Visual C++下的小波变换、滤波器和一般的图像处理操作。-Wavelets, filters and general functions for image processing.

小波分形草程序源码-Wavelet Fractal grass procedures FOSS

VC实现的小波算法-VC wavelet algorithm

FWT_Stat用信号的平稳位移计算二进小波正变换 MM2_DWT图像的二进小波变换的模极大-FWT_Stat using signal to the smooth displacement dyadic wavelet transform is the two images MM2_DWT Progressive wavelet transform modulus maxima

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-Based on the gabor filter matlab, select the target band, a good signal processing tools

小波变换的快速离散算法实现及应用-wavelet transform fast discrete algorithm and its application

D5/3提升整数小波，可实现图像的无损压缩，包括一维和二维图像整数小波变换-D5/3 integer wavelet upgrade can realize lossless image compression, including the one-dimensional and two-dimensional image integer wavelet transform

小波变换的由来及概念，怎么样计算小波变换，直观的形象的理解小波变换。-Wavelet transform and the concept of the origin, how the calculation of wavelet transform, intuitive understanding of the image wavelet transform.

水波纹，非常逼真的实现方法，从codeproject上得到的灵感，现已实现！-Water ripple, very realistic way to realize, from the codeproject been the inspiration, now realize!

关于二维小波变换的程序 [精华] 说明：此算法重在概念，速度并不是很快。因为FOR循环的缘故。此程序从循环矩阵的观点出发，把圆周卷积和快速幅里叶变换建立了联系。实现了分解和无失真重构。它只做了一层分解，即将256x256图形分解成为64x64的四个图形，避免了使用WKEEP()的困惑。主要思想为用小波滤波器族构造正交阵W,变换写为B=W*A*W ,反变换为：A=W *A*W,这与所有正交变换无异。W为循环正交矩阵，因此可用FFT实现快速运算，难点就在重构矩阵上。若用矩阵概念明确，一个共扼转置可以搞顶。但FFT的使用必须找到与分解序列的关系。-on Wavelet Transform procedures [best] Note : This algorithm important concept is not speed quickly. Because FOR cycle nowadays. This program cycle from the point of view of Matrix, circular convolution and fast Fourier transform is established links. To achieve the decomposition and reconstruction without distortion. It only had one decomposition, 256x256 graphics will be 64x64 decomposition of the four graphics, avoiding the use of WKEEP () confusion. The main idea of using wavelet filter generator orthogonal array W, write to transform B = W* A* W, anti-Transform : A = W* A* W, which is orthogonal transformation all the same. W cycle orthogonal matrix, can be used to achieve rapid FFT computation, on the dif