leidaxinhaoyuchuli

小波十讲中文、切边优化版本，适合直接在kindle阅读(<Ten Lectures on Wavelets>)

说明：  短时傅里叶变换的MATLAB实现代码，有效完成时频分析(matlab implementation code of short-time Fourier transform to effectively complete time-frequency analysis)

说明：  基于vc++6.0 的图像对比度拉伸操作(vc 6.0 on the image contrast stretching operation)

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)

使用DCT和小波变换的数字音频水印生成/解码程序，可存活于方差0.05的高斯白噪声及不同程度的剪切。内含程序/测试文件介绍。(Digital wartermarking encoding/decoding program with DCT and wavelet transform. nice robustness against Gaussian noise with variance less than 0.05 and any type of cropping attack. functions/test files introduction included)

使用小波包变换分析信号的MATLAB程序, 使用小波包变换分析两个信号的特征向量和各频率成分的功率谱，很完整的一个应用程序 (Signal using wavelet packet analysis of MATLAB program, using wavelet packet analysis of two signal eigenvectors and the power spectrum of each frequency component, an application is complete)

这篇关于从心电信号提取呼吸信号的调查，一共介绍了三种方法，即：频率提取，滤波器滤波，小波变换，三种方法。(This extract on the respiratory signal from ECG investigation, presented a total of three methods, namely: the frequency extraction filter filtering, wavelet transform, three ways.)

根据采集的时域数据，做fft得到其频谱，并计算其功率谱密度(According to the collected time-domain data, the frequency spectrum is obtained by FFT and the power spectral density is calculated.)

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

此程序仿真了一个被污染了的信号源，然后通过小波去噪，成功取出了信号中的杂波(This procedure simulation has polluted the supply oscillator, then through the wavelet denoising, the success took out in the signal clutter )