waveletvideo_coding

本文实现基于贝叶斯(BAYES)阈值的小波(WAVELET)图像去噪算法，相比较其它的小波去噪算法而言，效果非常好(This realization based on Bayesian (BAYES) Threshold the wavelet (WAVELET) image denoising algorithm, compared with other wavelet denoising algorithm, the effect was very good)

its c-wavelet transform

这是小波包程序，好不容易从网上下下的，希望对大家有用！可直接使用。 (Wavelet packet, and finally from the Internet under the hope that useful! Can be used directly.)

说明：  小波变换技术与工程实践：图像的小波变换(MFC框架下)(Wavelet transform technology and engineering practice: Image Wavelet Transform (MFC framework))

基于gabor小波变换的论文，对做人脸识别很有用(Gabor wavelet transform-based papers, do face recognition is useful)

旨在用C语言程序和MATLAB程序通过MUSIC算法对“信号提取”进行实现，结果证明程序运行效果良好，分辨率较高(Designed with C language program and MATLAB program through the MUSIC algorithm " signal extraction" for implementation, the results show the program running well, higher resolution)

用于折射波处理时的to、θ函数法求界面深度，浅层地震(Used for refraction wave in processing, θ function method to ask interface depth, shallow earthquake)

Wavelet compression algorithm. Start by computing a complete wavelet-transform using the cohen-daubechies-fevereau (2,2) biorthogonal wavelet (which incidentally seems to be the same as used for lossless JPEG2000, but they call it a (5,3)?).

在理解了离散小波变换的基本原理和算法的基础上，通过设计VC程序对简单的一维信 号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波和Haar小波变换，从而得到小波分解系数；再通过改变分解得到的各层高频系数进行信号的小波重构达到消噪的目的。在这一程序实现的过程中能直观地理解信号小波分解重构的过程和在信号消噪中的重要作用，以及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。(In understanding the basis of discrete wavelet transform basic principles and algorithms, through the design VC program Daubechies wavelet, Morlet wavelet and Haar wavelet transform after a simple one-dimensional signal plus a Gaussian white noise, resulting wavelet coefficients wavelet reconstruction signal again by changing the layers to achieve high-frequency coefficients decomposed noise cancellation purposes. Can intuitively understand wavelet decomposition and reconstruction process and an important role in signal de-noising in the process of implementation of this program, and in the high-frequency coefficients when the weight of the layers selected for signal processing coefficients denoising affected.)

its c-wavelet transform