WAVELET_JX

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.)

matlab提升小波变换与应用初步，包括小波变换的一些改进(the matlab enhance wavelet transform and application of the preliminary)

图像的小波去噪,给灰度图像加上一定量的高斯噪声,再采用小波变换的方法去噪,计算去噪前后图像的PSNR。(Wavelet Denoising images to grayscale images coupled with a certain amount of Gaussian noise, and then using the method of wavelet transform denoising calculated before and after denoising images PSNR.)

小波阈值去噪 软阈值去噪和硬阈值去噪的比较(Wavelet thresholding soft thresholding and hard thresholding comparison)

小波变换与分数傅里叶变换理论与应用(冉启文）电子版，介绍了小波变换、多分辨分析、小波构造、时频分析、小波包、塔式算法、分数傅立叶变换等内容，是一本不错的教材。(Wavelet Transform and Fractional Fourier Transform Theory and Applications (Ran text) electronic version, introduced the wavelet transform, multiresolution analysis, wavelet construction, time-frequency analysis, wavelet packet, tower algorithm, the fractional Fourier transform, etc., is one The good teaching materials.)

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.)

用于计算一个实数或复数序列的离散傅里叶变换和一个复数序列的逆离散傅里叶变换(fast fourier transform subroutine for real valued series)

《MATAB小波分析（第2版）》张德丰 书籍配套所有源代码("MATAB wavelet analysis (Second Edition)" - Zhang Defeng, books supporting all the source code)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing

小波变换模极大值重建信号的matlab源程序，来源于胡广书的《现代信号处理教程》附属光盘(Wavelet transform modulus maxima reconstruction signal matlab source code, derived from HU-book " modern signal processing tutorial subsidiary Disc)