基于多级树集合分裂排序（spiht）的图像压缩算法

主要讲述小波变换在信号分析和图像处理中的应用，英文版。(Wavelets applications in signal and image processing)

利用小波包分析信号的matlab程序，大家可以参考(Matlab program using wavelet packet analysis of the signal, we can refer to)

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.)

说明：  实现k-l变换，代码短，效率高，适合初学者进行上机练习，原理简单，比较容易懂和实现。(Achieve kl transformation, the code short, high efficiency, suitable for beginners to practice on the machine, simple in principle, relatively easy to understand and achieve.)

先进行提升小波变换，然后使用提升小波进行图像的分解和重构(Be carried out on lifting wavelet transform, and then use the images to enhance the wavelet decomposition and reconstruction)

说明：  用c++实现小波变换，参考matlab实现(Using C + + to realize wavelet transform, referring to matlab to realize)

小波变换的程序包括小波变化 小波分解小波包分解程序(Wavelet transform procedures include wavelet transform wavelet decomposition wavelet packet decomposition program)

通过设计Visual C程序源码对简单易懂的一维信号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波与Haar小波变换，从而的到小波分解系数；再通过改变分解的到的各层高频系数数进行信号的小波重构达到消噪噪的目的。在这一程序源码实现的过程中能直观地理解信号小波分解重构的过程与在信号消噪中的重要作用，和在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。 可直接 (Design Visual C program source code on a simple one-dimensional signal with a Gaussian white noise Daubechies wavelet, Morlet wavelet and Haar wavelet transform, and thus to the wavelet coefficients and then change the decomposition to each storey wavelet reconstruction frequency coefficient of the number of signal to noise canceling noise. In the process of realization of this program source code can be intuitively understood the signal wavelet decomposition and reconstruction process and an important role in signal denoising, and the selection coefficient in the high frequency coefficients of the layers of the weight of processing noise cancellation signal impact. Can be directly)

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。(Wavelet transform (WT) is a new transform analysis method. It inherits and develops the idea of localization of short-time Fourier transform and overcomes the shortcomings that window size does not vary with frequency. It can provide a frequency-dependent The Time-Frequency window is ideal for time-frequency analysis and processing of signals. Its main feature is that by transforming some features that can fully highlight some aspects of the problem, the localization of the time (space) frequency can be analyzed, and the signal (function) can be gradually and multi-scale refined by the scaling operation, finally reaching the high frequency Time subdivision, frequency subdivision at low frequencies, can automatically adapt to the requirements of time-frequency signal analysis, which can focus on any details of the signal to solve the difficulties of Fourier transform has become a major breakthrough in the scientific method since Fourier transform.)

图像处理中的小波变换C++源代码，含有使用文档和例子，非常全面！(image processing wavelet transform C source code, documentation and with the use of examples, very comprehensive!)