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主要用于图像经小波变换压缩图像，并计算压缩后的图像压缩性能即压缩比和信噪比(Mainly used for image compression by wavelet transform image, and calculate the compressed image compression performance that the compression ratio and signal to noise ratio)

OFDM系统完整程序，小波/小波包变换在OFDM中的应用的程序。(Complete program of the OFDM system, wavelet/wavelet packet transform in OFDM applications program.)

2维小波变换经典程序 此示意程序用DWT实现二维小波变换(wavelet transform two-dimensional classical procedures indicate this procedure using 2D DWT wavelet transform)

实现盲源信号的分离，在一个信号源中提取出有用的信号。(Blind source signal separation, in a signal source to extract useful signals.)

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)

该源代码又称双树复小波变换源代码，它不仅具有Gabor变换的六个方向选择性，而且有更小的冗余度，对图像处理的同行们肯定有很大的价值，可直接下载使用。(The source code also known as the Dual-Tree Complex Wavelet Transform source code, it not only has the Gabor transform of the six direction selectivity, but also has a smaller redundancy of image processing colleagues certainly have great value, can directly download使用.)

根据小波变换和噪声信号地能量分布特性，提出了一种先用小波变换对含噪图像进行多尺度分解，求出各尺度小波变换高频系数地噪声方差和阈值，利用各尺度地阈值对高频系数进行处理，然后利用小波变换系数重构图像，实现图像降噪地方法。(According to wavelet transform and noise signal and energy distribution characteristics, a first wavelet transform of the noisy image multiscale decomposition, obtained the high-frequency-scale wavelet transform coefficients and noise variance and thresholds, using the scale to the threshold value of the deal with high-frequency coefficients, and then the use of wavelet transform coefficients reconstructed image, to realize image denoising methods.)

一维信号的小波去噪,这是一个很好的基于小波变换的小波去噪，是针对一维的，去噪效果非常好！(1DWT wavelet denoide)

小波包的程序源代码,对于小波包的理解很有用，非常适合初学者！(Wavelet Packet program source code, useful for the understanding of wavelet packet, very suitable for beginners!)

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