▍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 )
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▍2. 1b3f6358
说明： CEEMDAN和EEMD等去噪方法的合集(CEEMDAN/EEMD/EMD:Collection of various denoising methods)
▍3. hist_match_demo
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.)
▍4. slantlet
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)
▍5. ECG
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.)
▍6. mediumvaluefilter
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!)
▍7. tuxiangbianh
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))
▍8. JPEG2000
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)
▍9. subtraction-speech-denoising
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 )
▍10. kalman
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.)
▍11. 3dwaveletebyrf
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)
▍12. 22222
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)
▍13. matlab_EZW
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.)
▍14. NumIntTest
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)
▍15. 非下采样小波变换的一个实现程序 UWT
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)
▍16. thresold
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))
▍17. wavelet_example
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,)
▍18. BCS-SPL-1.5-new
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
▍19. BCS-SPL-1.5-new
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
▍20. voltage-sag-lacote
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)
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