wavelet

java小波变换的实现，包括哈尔小波，以及在这基础上改进的小波实现。(The implementation of Daubechies wavelets.)

双树复数小波变换程序，内附详细的使用说明，有小波变换函数可以选择(Dual-Tree Complex Wavelet Transform process, containing detailed instructions, you can choose a wavelet transform function)

说明：  GHM多小波的变换与逆变换，包括预处理和后处理(GHM multi-wavelet transform and inverse transform)

kalman 卡尔曼滤波C代码 用于滤除高斯白噪声的滤波 测试真是可用。(The kalman Kalman filter C code for filtered white Gaussian noise filtering test is really available.)

小波压缩感知，在图像处理一维信号的应用，自小波变换(Wavelet compressed sensing, image processing application of the one-dimensional signal)

用于进行小波变换，对原始的光谱图像数据进行预处理(Used wavelet transform, the original spectral image data preprocessing)

比较了选取统一阈值和自己选取阈值，可以看出选取同一阈值去噪效果更好(You can see the benefits of a uniform threshold.)

2.1 傅里叶变换 2.1.1 经典傅里叶变换 2.1.2 傅里叶变换的基本性质 2.1.3 快速傅里叶变换 2.1.4 短时傅里叶变换 2.2 小波分析与多分辨率分析的历史 2.3 小波分析与傅里叶变换的对比 2.4 小波变换 2.4.1 连续小波变换 2.4.2 离散小波变换 2.4.3 高维小波连续变换 2.5 常用小波基函数 2.5.1 小波函数 2.5.2 小波函数系 2.5.3 复数小波 2.6 构造紧支撑正常小波基 2.7 多分辨率分析与小波构造 2.8 分析小波包 2.8.1 小波包的定义及性质 2.8.2 分解小波包的空间 2.8.3 小波包算法(2.1 Fourier transform 2.1.1 classical Fourier transform 2.1.2 The basic properties of the Fourier transform 2.1.3 Fast Fourier Transform 2.1.4 short time Fourier transform 2.2 wavelet analysis and multi-resolution analysis of the history of 2.3 Wavelet Analysis and comparison of the Fourier transform 2.4 Wavelet Transform 2.4.1 Continuous Wavelet Transform 2.4.2 Discrete Wavelet Transform 2.4.3 Continuous high-dimensional wavelet transform 2.5 Common wavelet basis function 2.5.1 wavelet function 2.5.2 Department of wavelet function 2.5.3 Complex Wavelet 2.6 Construction of compactly supported wavelets normal More than 2.7 resolution analysis and wavelet construction 2.8 Analysis of Wavelet Packet 2.8.1 Definition and properties of wavelet packet 2.8.2 wavelet packet decomposition space 2.8.3 Wavelet Packet Algorithm)

说明：  小波变换技术与工程实践：图像的小波变换(MFC框架下)(Wavelet transform technology and engineering practice: Image Wavelet Transform (MFC framework))

小波变换的模极大值重建信号源程序 非常具有参考价值(wavelet transform modulus maxima reconstruction signal source is a very valuable reference)