用于图像的多尺度分析，shapelet变换用matlab 编程实现

vc编码的小波变换程序-vc wavelet transform coding procedures

这是我学习图像处理时采用小波理论学习用过的学习资料，相信对大家会有帮助的-this to be my image processing used wavelet theory study used information on the study, we believe that the right will be helpful

image noise reduction by level dependent thresholding

小波算法vc实现2-wavelet algorithm to achieve two vc

Wavelet transform routines

各种数字图象处理方法（锐化，小波变换，水印嵌入等等）。-variety of digital image processing methods (sharpening, wavelet transform, watermark embedding etc.).

一维信号离散小波分解与重构的VC程序，很经典的一个-One-dimensional discrete wavelet signal decomposition and reconstruction of the VC process, it is the classic one

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

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.

调试通过的2维fft算法，含一个.cpp文件，一个.h文件