dwb_fusion

说明：  小波变换和压缩感知中的OMP算法比较，是一种比较新的算法(Wavelet transform and compression perception of the OMP algorithm comparison, is a relatively new algorithm)

图像的压缩感知进行稀疏化时，首先需要进行创建字典，本程序就可以创建gabor字典(to create the gabor dicitionary)

用matlab编写找到向量维度的波峰波谷的值.(Prepared using matlab to find vector value of the dimension of the peaks and valleys.)

基础应用基于小波变换阈值去噪的MATLAB源代码（包含小波包去噪程序）(Based on the application based on wavelet transform threshold denoising MATLAB source code (including the wavelet packet denoising procedures ))

基于小波变换的图像压缩感知 并且进行OMP重建 对压缩感知的性能与采样点数进行性能分析与比较(Image compression based on wavelet transform perception and the OMP reconstruction performance of compressed sensing and sample analysis and comparison of performance points)

在理解了离散小波变换的基本原理和算法的基础上，通过设计VC程序对简单的一维信 号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波和Haar小波变换，从而得到小波分解系数；再通过改变分解得到的各层高频系数进行信号的小波重构达到消噪的目的。在这一程序实现的过程中能直观地理解信号小波分解重构的过程和在信号消噪中的重要作用，以及在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。(In understanding the basis of discrete wavelet transform basic principles and algorithms, through the design VC program Daubechies wavelet, Morlet wavelet and Haar wavelet transform after a simple one-dimensional signal plus a Gaussian white noise, resulting wavelet coefficients wavelet reconstruction signal again by changing the layers to achieve high-frequency coefficients decomposed noise cancellation purposes. Can intuitively understand wavelet decomposition and reconstruction process and an important role in signal de-noising in the process of implementation of this program, and in the high-frequency coefficients when the weight of the layers selected for signal processing coefficients denoising affected.)

matlab的小波变换源代码 可以用来做图像分析，融合 (Matlab wavelet transform source code can be used for image analysis, fusion)

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 )

基于小波变换的嵌入式零树小波编码算法,可以多等级压缩图像(The embedded zerotree wavelet coding algorithm based on wavelet transform can compress images at multiple levels.)

小波变换(wavelet transform，WT)是一种新的变换分析方法，它继承和发展了短时傅立叶变换局部化的思想，同时又克服了窗口大小不随频率变化等缺点，能够提供一个随频率改变的"时间-频率"窗口，是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征，能对时间(空间)频率的局部化分析，通过伸缩平移运算对信号(函数)逐步进行多尺度细化，最终达到高频处时间细分，低频处频率细分，能自动适应时频信号分析的要求，从而可聚焦到信号的任意细节，解决了Fourier变换的困难问题，成为继Fourier变换以来在科学方法上的重大突破。(Wavelet transform (WT) is a new transform analysis method. It inherits and develops the idea of localization of short-time Fourier transform and overcomes the shortcomings that window size does not vary with frequency. It can provide a frequency-dependent The Time-Frequency window is ideal for time-frequency analysis and processing of signals. Its main feature is that by transforming some features that can fully highlight some aspects of the problem, the localization of the time (space) frequency can be analyzed, and the signal (function) can be gradually and multi-scale refined by the scaling operation, finally reaching the high frequency Time subdivision, frequency subdivision at low frequencies, can automatically adapt to the requirements of time-frequency signal analysis, which can focus on any details of the signal to solve the difficulties of Fourier transform has become a major breakthrough in the scientific method since Fourier transform.)