Y649678

Tetrolet变换的原代码，一种稀疏表示的小波变换，由haar变换改进得到(Tetrolet transform the original code, a sparse representation of the wavelet transform, haar transform improved)

运行emd,得到输入信号X的固有模态函数（imf），每列代表着信号X的一阶分量，X就是我们采集的信号。 hhtdn是hht去噪的程序，可以不看，仅看emd 和 findpeaks wvtdn是wavelet去噪程序，如果只是分解信号，仅用下面的语句即可 [C,L] = wavedec(x_noise,dl,wn); x_noise就是我们采集的信号，dl是分解的阶数，wn是分解的母小波名称(% Empiricial Mode Decomposition (Hilbert-Huang Transform) % imf = emd(x) % Func : findpeaks)

利用matlab中的小波变换函数，对图像进行小波降噪，并分析降噪效果。(The wavelet transform using matlab function, the image wavelet noise reduction, and analyze the noise reduction effect.)

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.)

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)

contourlet图像融合的matlab源代码，很有用，毕业设计的参考(contourlet image fusion matlab source code, useful, graduated from the reference)

提升小波变换程序， %LWT Lifting wavelet decomposition 1-D. % LWT performs a 1-D lifting wavelet decomposition % with respect to a particular lifted wavelet that you specify.(lifting wavelet transform procedures% Local Walsh Transform Lifting wavelet decomposition 1-D. % Local Walsh Transform performs a 1-D lifting wavelet decomposition% with respect to a particular lifted wavelet that you specify.)

说明：  小波变换和压缩感知中的OMP算法比较，是一种比较新的算法(Wavelet transform and compression perception of the OMP algorithm comparison, is a relatively new algorithm)

说明：  利用小波变换去除图像中噪声，对比其他去噪方法(Using Wavelet Transform to Remove Noise from Image)

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.)