小波变换

一篇很好的小波变换英文入门资料，附有注释，对想了解小波变换的初学者十分有益。(A good wavelet transform introductory information in English, annotated, very useful for beginners want to learn wavelet transform.)

关于小波包变化的，特征提取的一些程序，可直接运行成功。(Wavelet packet feature extraction)

使用四元数小波变换图像流估计。是对“Coherent Multiscale Image Processing using Dual-Tree Quaternion Wavelets”的实现。代码很详细。(Use quaternion wavelet transform image flow estimation. Is the " Coherent Multiscale Image Processing using Dual-Tree Quaternion Wavelets" implementation. Code is very detailed.)

wavelet transforms in advanced forms

this watermarking is implemented using wavelet transform..

离散小波变换的简单程序，适合初学者学习，附有通用函数。(Simple procedures discrete wavelet transform, suitable for beginners to learn, with common functions.)

基于多级树集合分裂排序（spiht）的图像压缩算法(Image compression algorithm based on a collection of multi-level tree split-Sort (spiht) of)

说明：  GHM多小波的变换与逆变换，包括预处理和后处理(GHM multi-wavelet transform and inverse transform)

数字基带传输基带信号，指未经调制的信号。这些信号的特征是其频谱从零频或很低频率开始，占据较宽的频带。 基带信号在传输前，必须经过一些处理或者某些变换（如码型变换、波形和频谱变换）才能送入信道中传输。处理或变换的目的是使信号的特性与信道的传输特性相匹配。 数字基带信号是消息代码的波形表示。表示形式有多种，有单极性、双极性波形，归零和非归零波形，差分波形、多电平波形之分，各自有不同的特点。 研究数字基带信号通过加性高斯白噪声信道的传输，并且假设信道除了叠加高斯噪声外，不会对信号产生其他失真。(matlabSimulation(pulse amplitude modulation) modulation signal in AWGN (additive white Gaussian noise AWGN (Additive White Gaussian Noise)) under the channel bit error rate and bit error rate performance, and compared with the theoretical value.)

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