segtool.tar

wavelet transforms in advanced forms

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)

its c-wavelet transform

基于小波变换的通信信号码元速率估计.pdf(Estimated rate based on the communication signal element wavelet transform. Pdf)

嵌入式小波图像编码即EZW是最近很流行的一种编码方式，我的程序是用matlab实现对EZW的描述。(Embedded Wavelet EZW image coding that is very popular recently as a way of coding, I use the matlab process is to achieve a description of the EZW.)

与labview之间的联系 在小波变换方面(Contact between labview in terms of the wavelet transform)

分别用Pisarenko谐波估计法、MUSIC算法和ESPRIT算法对三正弦叠加高斯白噪声信号源进行频率估计，附件中有代码说明，编译环境为matlab。(Respectively Pisarenko harmonic estimation, MUSIC algorithm and the ESPRIT algorithm superimposed on the three sine Gaussian white noise signal source for frequency estimation, in the annex to the code description, the compiler environment matlab.)

说明：  使用小波变换和神经网络的混合图像压缩，希望对大家有所帮助(Using wavelet transform and neural network hybrid image compression, I hope all of you to help)

说明：  关于小波包分析的图像绘制，小波分析，三次分解，单支重构 故障信号(Image registration algorithms based on piecewise linear interpolators (spatiotemporal gradients or block matching) are analyzed to determine subpixel registration accuracy.)

数字图像获取, 处理及实践应用源代码——很多有用的图像处理的源代码：图像显示（原图、抖动），图像增强（灰度变换、直方图均衡、多种滤波器、伪彩色增强等），图像复原（运动模糊、逆滤波等等），图像变换（傅立叶变换、快速傅立叶变换、离散余弦变换、沃尔什变换、霍特林变换、小波变换、小波反变换），图像编码（霍夫曼编码、香浓-费诺编码、算术编码、位平面编码等），图像识别。(Digital image acquisition, processing and practical application source code- a lot of useful image processing source code: Image Display (image, jitter), image enhancement (gray-scale transformation, histogram equalization, multi-filter, pseudo-color enhancement, etc. ), image restoration (motion blur, inverse filtering, etc.), image transform (Fourier transform, fast Fourier transform, discrete cosine transform, Walsh transform, Hotelling transform, wavelet transform, wavelet transform), image coding (Huffman coding, Shannon- Fenno coding, arithmetic coding, bit plane coding, etc.), image recognition.)