▍1. fftfenxi
短时傅里叶变换 快速傅里叶变换对音频信号处理 频谱分析 (fast fft )
说明: CEEMDAN和EEMD等去噪方法的合集(CEEMDAN/EEMD/EMD:Collection of various denoising methods)
说明: 利用ENVI/IDL,对两张影像进行直方图规范化,均衡化。(The histogram of the two images is normalized and balanced.)
图像多尺度分析,小波变换的进一步发展slantlet变换代码,用matlab编写有使用说明(slantlet code)
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.)
中值滤波源程序,非常好用。程序有说明很容易看懂!(median filtering source, very handy. Note procedure is easy to read!)
离散小波变换与离散小波反变换 快速小波变换(Mallat小波分解算法):对一幅图像做2级小波分解(离散小波变换)与合成(离散小波反变换)(Discrete wavelet transform and discrete wavelet inverse transform fast wavelet transform (Mallat wavelet decomposition): For an image to do 2 wavelet transform (DWT) and synthesis (discrete wavelet inverse transform))
说明: 小波变化代码,是比较好的测试向量,包括位平面编码和MQ(Wavelet transform code, is a better test vectors, including the bit plane coding and MQ)
一种基于改进谱减法的语音去噪新方法,对于想学习谱减法的同学有很大的帮助(A kind of based on improved spectral subtraction speech denoising, a new method to want to learn the spectral subtraction classmates have a lot of help )
kalman 卡尔曼滤波C代码 用于滤除高斯白噪声的滤波 测试真是可用。(The kalman Kalman filter C code for filtered white Gaussian noise filtering test is really available.)
这是一本关于高维(三维以上)小波的书,对做三维的朋友应该有参考价值,所以特意贡献出(This is one of the high-dimensional (3-D above) wavelet, the book right to do 3D friends should have reference value, So deliberately contributing)
小波变换去噪,软阈值和硬阈值,以及自己的设计的阈值函数来去噪,毕业设计全套。很全面的资料,绝对最全面的资料,我保证(Wavelet denoising, soft thresholding and hard thresholding, as well as design their own threshold denoising function, the design of a full set of graduation. Comprehensive information is absolutely the most comprehensive information, I promise)
嵌入式小波图像编码即EZW是最近很流行的一种编码方式,我的程序是用matlab实现对EZW的描述。(Embedded Wavelet EZW image coding that is very popular recently as a way of coding, I use the matlab process is to achieve a description of the EZW.)
加速度振动信号积分为速度振动信号输出,如需要可再一次积分,变换为位移信号(Vibration signal of the acceleration integral transform)
非下采样小波变换的一个实现程序,对边缘和纹理提取效果较好(Nonsubsampled wavelet transform of an implementation program, better edge and texture extraction effect)
说明: 基于小波变换阈值去噪的MATLAB源代码(包含小波包去噪程序)(threshold based on wavelet transform Denoising MATLAB source code (including wavelet denoising packet procedure))
小波变换的例子, 小波变换的例子, 小波变换的例子, 小波变换的例子,(wavelet transform example, wavelet transform example, Wavelet Transform example, wavelet tra nsform example,)
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
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
基于小波变换的电压暂降定位 利用模极大值原理实现暂降起止时刻的准确检测(voltage sag lacote based on wavelet transform Used to achieve maximum modulus principle sag beginning and ending time of the accurate detection)