基于小波变换的信号奇异性检测及去噪例程

图像的小波去噪,给灰度图像加上一定量的高斯噪声,再采用小波变换的方法去噪,计算去噪前后图像的PSNR。(Wavelet Denoising images to grayscale images coupled with a certain amount of Gaussian noise, and then using the method of wavelet transform denoising calculated before and after denoising images PSNR.)

说明：  有小波分解求小波熵，还有小波包技术求解信号特征，适应与分析脑电信号特征(Seeking a wavelet decomposition wavelet entropy, and wavelet packets technique to solve the signal characteristics, and analysis of EEG characteristics of adaptation)

说明：  2.1 傅里叶变换 2.1.1 经典傅里叶变换 2.1.2 傅里叶变换的基本性质 2.1.3 快速傅里叶变换 2.1.4 短时傅里叶变换 2.2 小波分析与多分辨率分析的历史 2.3 小波分析与傅里叶变换的对比 2.4 小波变换 2.4.1 连续小波变换 2.4.2 离散小波变换 2.4.3 高维小波连续变换 2.5 常用小波基函数 2.5.1 小波函数 2.5.2 小波函数系 2.5.3 复数小波 2.6 构造紧支撑正常小波基 2.7 多分辨率分析与小波构造 2.8 分析小波包 2.8.1 小波包的定义及性质 2.8.2 分解小波包的空间 2.8.3 小波包算法(2.1 Fourier transform 2.1.1 classical Fourier transform 2.1.2 The basic properties of the Fourier transform 2.1.3 Fast Fourier Transform 2.1.4 short time Fourier transform 2.2 wavelet analysis and multi-resolution analysis of the history of 2.3 Wavelet Analysis and comparison of the Fourier transform 2.4 Wavelet Transform 2.4.1 Continuous Wavelet Transform 2.4.2 Discrete Wavelet Transform 2.4.3 Continuous high-dimensional wavelet transform 2.5 Common wavelet basis function 2.5.1 wavelet function 2.5.2 Department of wavelet function 2.5.3 Complex Wavelet 2.6 Construction of compactly supported wavelets normal More than 2.7 resolution analysis and wavelet construction 2.8 Analysis of Wavelet Packet 2.8.1 Definition and properties of wavelet packet 2.8.2 wavelet packet decomposition space 2.8.3 Wavelet Packet Algorithm)

小波基contourlet变换 其中包含spiht编码 去噪 纹理识别(Wavelet transform contourlet transform includes SPIHT coding and denoising texture recognition.)

wavelet transforms in advanced forms

小波方法进行降噪，阈值函数来完成不同设计，核心程序如下面的所示，使用的小波基有coif5，db3，sym4，bior2.4，haar，rbio(Wavelet method for noise reduction, design to complete different threshold function, the core program as shown below, using the wavelet base coif5, db3, sym4, bior2.4, haar, rbio )

说明：  基本的S变换matlab实现 用于时频分析 为初学者提供基本代码(S transformation for time-frequency analysis to provide basic code for beginners)

利用小波分析原理对某信号进行分解与重构，并可提取指定层数上的高、低频信号。(A signal is decomposed and reconstructed by using wavelet analysis principle, and high and low frequency signals can be extracted from the specified number of layers.)

二代小波harr变换整数小波变换，适合图像处理专业人员参考(Second generation wavelet transform Harr integer wavelet transform for image processing professionals reference)

离散小波变换是指在特定子集上采取缩放和平移的小波变换，是一种兼具时域和频域多分辨率能力的信号分析工具。此变换运用可以缩放平移的小波代替固定的窗进行计算分析，主要应用于信号编码和数据压缩。(Discrete wavelet transform refers to the specific subset take scaling and translation on the wavelet transform, is a kind of multi-resolution ability both time domain and frequency domain signal analysis tool. This transformation can use zoom translational wavelet calculation and analysis instead of the fixed window, mainly used in signal coding and data compression. )