ex001

说明：  shearlet变换matlab工具箱，可用于图像降噪、融合(Shearlet transform matlab toolbox can be used for image denoising and fusion)

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.)

基于小波变换的包络分析用于轴承故障诊断的源代码(Bearing envelope analysis)

实现基于离散小波（dwt）的奇异值（svd）分解算法的实现(Decomposition algorithm based on singular value of the discrete wavelet (dwt) (svd))

说明：  利用小波变换去除图像中噪声，对比其他去噪方法(Using Wavelet Transform to Remove Noise from Image)

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.)

本代码给出了图像的二维离散小波变换分解、重构图像以及误差图像。(The code gives the two-dimensional image decomposition of discrete wavelet transform, image reconstruction and the error image.)

简单的方向滤波器， 带测试图的， 可以直接运行(Simple directional filter, with a test chart, you can directly run)

说明：  图像小波变换，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。(Image Wavelet Transform)

说明：  小波变换是一种新的变换分析方法，一个迅速发展的新领域(Wavelet transform is a new transform analysis method and a rapidly developing new field)