psnr_noise

希尔伯特变换和小波变换检测相位差的matlab程序和一些资料(Hilbert transform and wavelet transform to detect the phase difference matlab program and some of the information)

单小波变换应用于图像压缩,及其应用于图像融合的MATLAB代码(Single-wavelet transform to image compression, image fusion and its application to the MATLAB code)

利用小波变换，通过设置阈值对图像进行去噪。 首先对图像用小波进行2层分解，然后通过设置硬阈值和软阈值的方法，对图像高频部分去噪。(The wavelet transform is used to denoise the image by setting threshold. Firstly, the image is decomposed into 2 layers by wavelet, and then the high frequency part of the image is denoised by setting the hard threshold and the soft threshold.)

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.)

说明：  该函数用于读取ecg信号，找到小波变换模极大序列(function to read the ECG signal, find wavelet transform modulus maxima sequence)

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)

使用四元数小波变换图像流估计。是对“Coherent Multiscale Image Processing using Dual-Tree Quaternion Wavelets”的实现。代码很详细。(Use quaternion wavelet transform image flow estimation. Is the " Coherent Multiscale Image Processing using Dual-Tree Quaternion Wavelets" implementation. Code is very detailed.)

this is mat lab wavelet paper

图像处理中的小波变换C++源代码，含有使用文档和例子，非常全面！(image processing wavelet transform C source code, documentation and with the use of examples, very comprehensive!)

某一直升机齿轮箱上的早期损伤的特征是，在伴随振动信号上产生一个可变周期的非平稳扰动信号。要求按此特征构造一组有物理意义的数据仿真早期故障信号，应用小波变换技术分析故障的特征和发生时刻。(a helicopter gearbox early on the characteristics of the injury, In addition to the vibration signal produced a variable cycle perturbation of non-stationary signals. Click here to request a group of tectonic significance of a physical early fault simulation data signals Application of wavelet transform technical analysis of the features and fault occurred moments.)