matlabwaveletthreshod

说明：  基于小波的图像分解与重构以及各种灰度图像。(Wavelet-based image decomposition and reconstruction, and various gray scale images.)

小波变换的程序包括小波变化 小波分解小波包分解程序(Wavelet transform procedures include wavelet transform wavelet decomposition wavelet packet decomposition program)

基于高阶累积量的MUSIC算法仿真，有直观结果可供参考(MUSIC algorithm simulation based on higher order cumulants, intuitive results available)

Harr小波具有紧支撑、零调和性和优良的正交性等优点，因此选择它作为小波变换的基函数。对采样数据进行4层小波包变换得到16维的特征样本。(Harr wavelet has compact support, zero reconcile and excellent orthogonality, etc., so choose it as the base function of wavelet transform. 4 layers of sampled data wavelet packet transform feature samples 16 dimensions.)

说明：  53小波提升的编解码代码，可用于图像的53小波变换(53)

脉搏信号分析 （1）设计滤波器，实现对脉搏信号的噪声抑制和基线纠漂； （2）时域分析：波形特征检测； （3）功率谱分析：对消噪后的信号进行功率谱分析。要求计算信号的功率谱，功率谱峰值，峰值频率。 (Pulse signal analysis (1) design the filter, to achieve the pulse signal noise suppression and baseline drift correction (2) time-domain analysis: waveform feature detection (3) power spectral analysis: noise cancellation signal after the power spectrum analysis . Require calculation of the signal power spectrum, peak power spectrum, peak frequency.)

说明：  主要针对道路提取程序，使用二值法和形态法(Road extraction procedures, using binary and morphological methods)

curvelet变换最新工具包，含有快速离散曲波变换的所有形式（包括基于usfft和wrapping和3dfdct）。功能强大，是学习曲波变换的好帮手。(curvelet transform the latest kit, containing the fast discrete curvelet transform all forms (including those based on usfft and wrapping and 3dfdct). Powerful learning curvelet transform is a good helper.)

使用matlab实现的BPSK信号产生和加入噪声前后的频谱对比以及FFT测频和一阶差分法测频对比(Using matlab to achieve the BPSK signal generator and adding noise before and after the spectrum contrast and FFT frequency measurement and first-order differential method to measure the frequency contrast)

运行emd,得到输入信号X的固有模态函数（imf），每列代表着信号X的一阶分量，X就是我们采集的信号。 hhtdn是hht去噪的程序，可以不看，仅看emd 和 findpeaks wvtdn是wavelet去噪程序，如果只是分解信号，仅用下面的语句即可 [C,L] = wavedec(x_noise,dl,wn); x_noise就是我们采集的信号，dl是分解的阶数，wn是分解的母小波名称(% Empiricial Mode Decomposition (Hilbert-Huang Transform) % imf = emd(x) % Func : findpeaks)