yizhixijiexishu

很好的继续教育教材，提升素质必备啊！不可错过！十分的好(Good continuing education materials, enhance the quality of essential ah!)

说明：   这是一个用小波神经网络进行非线性函数逼近的例子，拟合效果非常好，保证能运行！有详细注释！我是参考《青岛海洋大学学报》 2001年第1期 一种基于BP算法学习的小波神经网络编写的，大家可以针对原文更好的理解我的程序！(This is a wavelet neural network with nonlinear function approximation example of fitting results is very good, guaranteed to run! Detailed Notes! I refer to Journal of Ocean University of Qingdao，2001 ，“a learning algorithm based on BP wavelet neural network ”, you will understand me better for program!)

wavelet transform,continuous wave let transform,discrete wavelet transform

基于小波变换的图像编码，已经使用，效果很好。(Image coding based on wavelet transform has been used with good results.)

心电信号的读入，滤波，以及小波变换并重构。(ECG reads, filtering, and wavelet transform and reconstruction.)

小波变换的程序包括小波变化 小波分解小波包分解程序(Wavelet transform procedures include wavelet transform wavelet decomposition wavelet packet decomposition program)

基于小波变换的信号奇异性检测，小波模极大值的计算(The signal singularity detection based on wavelet transform )

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)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing

说明：  地震勘探中小波变换，去噪，对初学者容易上手，运用maltlab编写，运行也方便，(Wavelet transform and denoising in seismic exploration)