tuxiangshuyan

Gabor小波变换的matlab实现，可以完成对图像的纹理特征提取，广泛应用于图像处理，模式识别等方向(Gabor wavelet transform matlab implementation can complete the image texture feature extraction is widely used in image processing, pattern recognition and so the direction of)

基于论文<<Blur Detection for digital images using wavelet transform>>,使用Haar小波变换实现图像模糊度检测。(Based on paper <Blur Detection for digital images using wavelet transform> > , Using the Haar wavelet transform of image blur detection.)

使用DCT和小波变换的数字音频水印生成/解码程序，可存活于方差0.05的高斯白噪声及不同程度的剪切。内含程序/测试文件介绍。(Digital wartermarking encoding/decoding program with DCT and wavelet transform. nice robustness against Gaussian noise with variance less than 0.05 and any type of cropping attack. functions/test files introduction included)

小波变换、小波逆变换代码，数字信号处理领域常用(Wavelet transform, wavelet inverse transform code, digital signal processing used)

说明：  Gabor小波提取特征,然后用支持向量机作分类器，可以用于掌纹，人脸，指纹识别(Gabor wavelet is used to extract features, and then SVM is used as classifier. It can be used for palmprint, face recognition and fingerprint recognition)

心电信号分析 （1）设计滤波器，实现对心电信号的噪声抑制和基线纠漂。 （2）时域分析：R波检测算法与实现. （3）功率谱分析：对消噪后的信号进行功率谱分析。要求计算信号的功率谱，功率谱峰值，峰值频率。 (ECG Analysis (1) design filters to achieve noise suppression of ECG and baseline drift correction. (2) time-domain analysis: R-wave detection algorithm and implementation (3) The power spectrum analysis: noise cancellation signal after the power spectrum analysis. Require calculation of the signal power spectrum, peak power spectrum, peak frequency.)

结合离散小波变换(DWT)技术，利用DWT与HVS相结合的良好特性，探索和建立了一种新的图像质量客观评价方法(taking advantage of the similarities of discrete wavelet transform (DWT) with HVS，a new methods for integrated image quality assessment)

说明：  带有多种小波基的小波变换程序，可用于图象识别，信号突变检测。(with multiple small Porgy wavelet transform procedures can be used to image recognition, signal mutation detection.)

Matlab 二进小波变换程序，求取模极大值程序(Matlab wavelet transform into two procedures, to strike maxima procedures)

有关小波包分解和介绍的PPT，适合于初学者快速掌握小波包分解相关问题。(the PPT of wavelet packet decomposition and the introduction , suitable for beginners to quickly master the wavelet packet decomposition.)