qwet1234

说明：  通过高斯函数导数检测图像边缘，实质是方向可调小波变换检测图像边缘（8个方向变换）。(Gaussian function through derivative edge detection in real terms is adjustable direction edge detection wavelet transform (change the direction of 8).)

可用于曲波变换的工具箱，用于含噪声的图像的去噪过程,图像分解，有例子可直接运行。只可用于学术研究目的。(the Latest curvelet transform lab tool ，Non-commercial research use for Academics. Image decomposition, there are examples can be run directly.)

图像滤波变换程序，上学期课程设计代码，绝对原创(Image filtering transform procedures, last semester curriculum design code, an absolute original)

说明：  针对不同采样频率和输入信噪比的心磁信号进行滤波操作，呈现出对同一输入信号采取不同滤波算法的去噪结果。(The filtering operation is performed on the magnetic heart signals of different sampling frequencies and input signal-to-noise ratios, showing the denoising results of different filtering algorithms for the same input signal.)

说明：  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)

自己写的小波图像压缩，包括普通压缩和序列图像压缩(himself wrote of wavelet image compression, including the compression and ordinary sequence Image Compression)

基于MATLAB的简易声音信号频谱分析仪设计源码及设计报告。可以实现音频信号的不同频段滤波。(MATLAB simple voice signal spectrum analyzer design source code and design reports. Can achieve a filtering of the different frequency bands of the audio signal.)

这个程序是基于小波变换的信号奇异性检测及去噪 的一些例程(using wavelet method and SVD to process the signal and denoise)

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