wavelet_lab2

提取图像显著图，小波融合 非常详细，有用(Extract image saliency map, wavelet fusion is very detailed, useful for)

小波包分析提取振动信号中的分解，系数重构特征频率，以及能量谱分析计算(Wavelet packet analysis to extract the vibration signal decomposition coefficient reconstruction of the characteristic frequency, and calculate the energy spectrum analysis)

离散小波变换是指在特定子集上采取缩放和平移的小波变换，是一种兼具时域和频域多分辨率能力的信号分析工具。此变换运用可以缩放平移的小波代替固定的窗进行计算分析，主要应用于信号编码和数据压缩。(Discrete wavelet transform refers to the specific subset take scaling and translation on the wavelet transform, is a kind of multi-resolution ability both time domain and frequency domain signal analysis tool. This transformation can use zoom translational wavelet calculation and analysis instead of the fixed window, mainly used in signal coding and data compression. )

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)

利用小波包分析信号的matlab程序，大家可以参考(Matlab program using wavelet packet analysis of the signal, we can refer to)

说明：  可以将目标矩阵进行线性褶积运算，用于数字信号处理(The target matrix can be linearly convoluted for digital signal processing)

小波变换(wavelet transform，WT)是一种新的变换分析方法，它继承和发展了短时傅立叶变换局部化的思想，同时又克服了窗口大小不随频率变化等缺点，能够提供一个随频率改变的"时间-频率"窗口，是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征，能对时间(空间)频率的局部化分析，通过伸缩平移运算对信号(函数)逐步进行多尺度细化，最终达到高频处时间细分，低频处频率细分，能自动适应时频信号分析的要求，从而可聚焦到信号的任意细节，解决了Fourier变换的困难问题，成为继Fourier变换以来在科学方法上的重大突破。(Wavelet transform (WT) is a new transform analysis method. It inherits and develops the idea of localization of short-time Fourier transform and overcomes the shortcomings that window size does not vary with frequency. It can provide a frequency-dependent The Time-Frequency window is ideal for time-frequency analysis and processing of signals. Its main feature is that by transforming some features that can fully highlight some aspects of the problem, the localization of the time (space) frequency can be analyzed, and the signal (function) can be gradually and multi-scale refined by the scaling operation, finally reaching the high frequency Time subdivision, frequency subdivision at low frequencies, can automatically adapt to the requirements of time-frequency signal analysis, which can focus on any details of the signal to solve the difficulties of Fourier transform has become a major breakthrough in the scientific method since Fourier transform.)

小波分析在心电信号去噪中的应用,小波变换在心电图数据处理中的应用研究(Wavelet analysis of ECG signal denoising)

说明：  计算小波分解后的能量及小波熵。里面还有OFDM信号和WPM信号的程序。(After calculating the energy of wavelet decomposition and wavelet entropy. There is also OFDM signal and the WPM signal process.)

这篇关于从心电信号提取呼吸信号的调查，一共介绍了三种方法，即：频率提取，滤波器滤波，小波变换，三种方法。(This extract on the respiratory signal from ECG investigation, presented a total of three methods, namely: the frequency extraction filter filtering, wavelet transform, three ways.)