xiaobo

dual tree complex wavelet transform

对图像进行小波变换的分解和重构的代码，包含以为信号的处理。( The image decomposition and reconstruction of wavelet transform code, including that the processing of the signal.)

小波分析用于图像融合和去噪；APES,MUSIC,LMS算法仿真实例；(Wavelet analysis is used to image fusion and noise；APES,MUSIC,LMS )

降水小波分析 用于分析降水序列的周期变化规律(Wavelet analysis of rainfall series for the analysis of the cycle variation of precipitation)

matlab提升小波变换与应用初步，包括小波变换的一些改进(the matlab enhance wavelet transform and application of the preliminary)

对信号f(x)进行三层小波变换，得到小波系数d1,d2,d3和逼近系数a3，我想求出各个频率段的能量（能量=小波系数的平方和），很不错的应用程序(The signal f (x) for three wavelet transform, wavelet coefficients d1, d2, d3 and approximation coefficients a3, I would like to find the energy of each frequency band (energy = sum of squared wavelet coefficients), very good application)

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

MORLET小波分析在MATLAB的环境下 适合初学者(MORLET waveLET ANYSIS)

实现小波包的n层分解与重构,以及简单的输入输出方式(Implementation of wavelet packet decomposition and reconstruction of N layer)

小波变换阈值去噪的文章，基于最大最小准则计算(Minimax Estimation, Adaptive Estimation, Nonparametric Regression, Density Estimation, Spatial Adaptation, Wavelet Orthonormal bases, BesovSpaces, Optimal Recovery.)