imagetransformcodes

去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信信号，避免噪声对心电信号特征点的识别与提取造成误判漏判 已通过测试。 (Remove the EMG interference mixed with the signal acquisition process in mind, frequency interference, baseline drift and noise channel signal to avoid noise caused by the misjudgment of the Missing has been tested on ECG feature identification and extraction.)

小波阈值法去噪的程序，其中有关于信噪比和均方根误差的公式(wavelet denoising)

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)

vc实现虚拟示波器，没有测试过不知道能用吧(vc virtual oscilloscope not tested do not know can)

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)

具有提升分解结构的整数离散变换，可用于基于提升格式的小波变换设计。(Enhance the breakdown structure of the integers with the discrete transform can be used for wavelet transform based on lifting scheme design.)

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)

实现对图像的gabor小波变换，利用了opencv的函数库，可实现方向和尺度等多项选择。(Achieve image gabor wavelet transform, using the opencv function library, enabling the direction and scale and many other options.)

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.)

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.)