EEG_WAVELET

数字图像获取, 处理及实践应用源代码——很多有用的图像处理的源代码：图像显示（原图、抖动），图像增强（灰度变换、直方图均衡、多种滤波器、伪彩色增强等），图像复原（运动模糊、逆滤波等等），图像变换（傅立叶变换、快速傅立叶变换、离散余弦变换、沃尔什变换、霍特林变换、小波变换、小波反变换），图像编码（霍夫曼编码、香浓-费诺编码、算术编码、位平面编码等），图像识别。(Digital image acquisition, processing and practical application source code- a lot of useful image processing source code: Image Display (image, jitter), image enhancement (gray-scale transformation, histogram equalization, multi-filter, pseudo-color enhancement, etc. ), image restoration (motion blur, inverse filtering, etc.), image transform (Fourier transform, fast Fourier transform, discrete cosine transform, Walsh transform, Hotelling transform, wavelet transform, wavelet transform), image coding (Huffman coding, Shannon- Fenno coding, arithmetic coding, bit plane coding, etc.), image recognition.)

wavelet transforms in advanced forms

理解有源滤波器的工作原理、性能特点。学习有源滤波器的设计方法。(Understand the working principle of active filter performance characteristics. Learning active filter design methods.)

二维连续小波变换，以及高维连续小波变化，简单易懂！！(Mainly includes two dimensional continuous wavelet transform and higher dimensional continuous wavelet transform and its application!!)

美国Rice大学提供的matlab 小波代码，要比matlab自带的matlab更易用(Rice University, USA matlab wavelet code comes than easier to use)

本程序应用小波变换进行电力系统的在线监测。电力系统在线监测中的检测信号含有大量的背景噪声，不便于检测。应用小波变换解决这一问题。程序中应用db3小波对信号进行5层分解，并得出了各层的细节信号与逼近信号。(This procedure based on wavelet transform power-line monitoring system. Online monitoring of power system test signal contains a lot of background noise, not easy to detect. Application of wavelet transform to resolve this problem. Application procedures for db3 wavelet signal decomposition layer 5, and arrive at different levels of detail signal and approximation signal.)

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

说明：  实现对离散信号的连续小波变换进行时频域分析，判断信号的稳定性(The continuous wavelet transform of the signal is analyzed in time and frequency domain)

主要仿真风力发电系统下matlab pwm整流及波形分析(Main simulation wind power systems matlab rectifier )