Wavelet_OMP

通过设计Visual C程序源码对简单易懂的一维信号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波与Haar小波变换，从而的到小波分解系数；再通过改变分解的到的各层高频系数数进行信号的小波重构达到消噪噪的目的。在这一程序源码实现的过程中能直观地理解信号小波分解重构的过程与在信号消噪中的重要作用，和在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。 可直接 (Design Visual C program source code on a simple one-dimensional signal with a Gaussian white noise Daubechies wavelet, Morlet wavelet and Haar wavelet transform, and thus to the wavelet coefficients and then change the decomposition to each storey wavelet reconstruction frequency coefficient of the number of signal to noise canceling noise. In the process of realization of this program source code can be intuitively understood the signal wavelet decomposition and reconstruction process and an important role in signal denoising, and the selection coefficient in the high frequency coefficients of the layers of the weight of processing noise cancellation signal impact. Can be directly)

现代控制理论，卡尔曼滤波器设计，simulink控制仿真(Modern control theory, Calman filter design, Simulink control simulation.)

使用DCT和小波变换的数字音频水印生成/解码程序，可存活于方差0.05的高斯白噪声及不同程度的剪切。内含程序/测试文件介绍。(Digital wartermarking encoding/decoding program with DCT and wavelet transform. nice robustness against Gaussian noise with variance less than 0.05 and any type of cropping attack. functions/test files introduction included)

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，反变换，以及提升小波变换，包括97小波，及53小波(Wavelet transform, inverse transform, as well as the lifting wavelet transform, including the 97 wavelet, and 53 wavelet)

可见光与红外图像融合技术的研究 其中多种算法在内(Infrared and visible image fusion research)

基于小波变换的图像拼接。 本程序利用小波变换的原理实现图像的上下，左右以及斜方向的无缝拼接(Wavelet-based image stitching. The procedure to achieve the principle of wavelet transform image from top to bottom, left and right oblique direction and seamless)

基于小波和插值的超分辨率图像重建算法，通过插值后将小波分解应用于图像中，再通过逆变换得到重构图像(And interpolation based on wavelet super-resolution image reconstruction algorithm by interpolating the image after the wavelet decomposition used, then the reconstructed image through the inverse transform)

bandelet denoising toolbox for matlab

实现小波变换，小波滤波器，小波除噪等功能。(To achieve wavelet transform, wavelet filters, wavelet denoising function)