An Introduction to Wavelets

简单的哈尔小波变换。程序实现的功能如下：输入图像，界面中可以看到图像经变换所产生的变化-Simple Haar wavelet transform. Realize the function of the procedure are as follows: the input image, the interface can be seen in images generated by the transformation changes

图像多尺度分析，小波变换的进一步发展slantlet变换代码，用matlab编写有使用说明-slantlet code

基于带参数整数小波变换可见数字水印，技术很前沿-This is a visible watermark sourcecode based on a integral wavelet transformation with parameters.

小波图象去噪综述谢杰成张大力徐文立-Wavelet Image Denoising vigorously Jie Zhang Xu Wenli

实现图像的正交变换，包括傅立叶变换，沃尔什变换，小波变换等-Realize the orthogonal transformation images, including Fourier transform, Walsh transform, wavelet transform, etc.

通过线性代数介绍小波。Frazier的经典著作，在小波书籍中，属于通俗易懂类。-Linear algebra through the introduction of wavelet. Frazier

vc++实现mallat算法,对小波变换的二维离散变换作了一些工作!值的一看!-vc achieve Mallat algorithm, the wavelet transform two-dimensional discrete transform made some work! Value of a view!

语音处理软件， GUI 界面，本人原创，频域，时域，可以看波形，WINDOW FUNCTION， 录音，-Voice-processing software, GUI interface, I created, the frequency domain, time domain, you can look waveform, WINDOW FUNCTION, recording,

小波变换具有良好的时―频局部性，是分析奇异信号的重要方法定点DSP在工程中的厘用士分背通，具实现小波变换可以满足工程是实时性的要求文中简要介绍了小波变换理论及算法，并结合TI公司的16位定点说明算法的实现。 -Wavelet transform has a good time- frequency localization, is to analyze the singular signal an important means of fixed-point DSP in engineering per cent pass with disabilities at the back with the realization of wavelet transform to meet the project is a real-time requirements of the article a brief introduction of wavelet transform theory and algorithm, combined with TI

ICA算法The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.-ICA algorithm:The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.