wavelet

bandelet denoising toolbox for matlab

说明：  可用于信号的EMD、EEMD、VMD分解，里面都加了自己的注释，通俗易懂(It can be used for signal EMD, EEMD, VMD decomposition. Because the algorithm adds its own annotations, it's easy to understand)

说明：  主要用于图像融合。 NSCT融合有利于更好的保持图像的边缘信息和轮廓结构，增强图像的平移不变性。(It is mainly used for image fusion. NSCT fusion is conducive to better preserve the edge information and contour structure of the image, and enhance the translation invariance of the image.)

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 )

基于多尺度奇异值分解的图像融合程序及一组待融合图像，以及一篇文献。(Image fusion program based on multiscale singular value decomposition and a set of images to be fused, and a document.)

希尔伯特变换和小波包结合的齿轮箱故障诊断方法，里面有具体的介绍小波包的分层方法，非常详细，可以下下来看看(Hilbert transform and wavelet packet of gearbox fault diagnosis method, there is detailed introduction inside the layered method of wavelet packet is very detailed, can down and have a look )

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.)

小波包分析提取振动信号中的特征频率，以及能量谱分析计算-(wavelet packet analysis vibration signal from the characteristic frequency, and the energy spectrum analysis )

说明：  提出了一种结合SVD的小波变换方法，对其在外弹道测量数据中的野值剔除进行了研究。对观测数据进行小波分解，将小波分解后的近似分量和细节分量组合实现相空间重构，作为SVD方法的输入观测矩阵，根据奇异 熵增量准则，对奇异值进行筛选，根据SVD逆变换重构原信号。这一方法克服了Hankel矩阵相空间构建方法数据 端点失真问题。以小波分解后分量重构的相空间可以满足正交性，进一步提高了SVD进行数据降噪和野值检测的精度。仿真数据和试验数据处理结果证明了这一方法的有效性。(Proposed a method of combining the SVD wavelet transform its outer ballistic measurement data in excluding outliers were studied. Observational data on wavelet decomposition, wavelet decomposition of the approximate combined component and detail component to achieve the phase space reconstruction, as the input observation matrix SVD method, based on singular entropy increment standards, singular value filter, according to the inverse transform heavy SVD structure of the original signal. This approach overcomes the Hankel matrix phase space construction method data endpoint distortion. Wavelet decomposition component of reconstruction phase space satisfy orthogonality, to further improve the SVD for data noise reduction and outlier detection accuracy. Simulation data and experimental data processing results prove the effectiveness of this approach.)

实现盲源信号的分离，在一个信号源中提取出有用的信号。(Blind source signal separation, in a signal source to extract useful signals.)