jixujiaoyu1

说明：  利用小波变换将不同分辨率的图像进行小波分解，将高频和低频系数分别处理融合，进行小波重构。(Image fusion with different resolutions based on wavelet transform.)

此为提供初学计算电磁学应用於色散介质的人，一维色散介质-Lorentz的程序(It is fortran code to provide a chance to learn the computational EM wave application of dispersive media for Lorentz model)

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)

图像融合的经典算法 IHS算法 PCA算法 加权融合算法等融合算法 程序可以直接运行 欢迎大家下载(The classical algorithm IHS algorithm PCA algorithm weighted fusion algorithm, such as image fusion fusion algorithm program can be run directly are welcome to download)

小波变换的数字水印算法,附有论文和源程序.(Wavelet Transform Digital Watermarking Algorithm with thesis and source code.)

说明：  基于二进小波变换的图像小波模极大值检测，以及交替投影法重建(Based on dyadic wavelet transform modulus maxima of wavelet image detection, as well as the alternating projection reconstruction)

对图像进行脊波变换，包含实例，与小波变换的对比实例(Ridgelet image transformation, including examples of the contrast with the example of wavelet transform)

由仿真结果时域和频域图可以看出，工频50Hz干扰信号很好被滤除掉，说明所设计的自适应滤波器效果较好。(It can be seen by the domain and frequency domain simulation results in Figure, the frequency 50 Hz interference signals very well be eliminated, the design of the adaptive filter is better.)

本文研究的是利用奇异性指数实现对设备状态监视和故障预警功能。首先用LabVIEW 采集传感器电信号，然后对采集到的电信 号进行小波变换得到信号包络奇异点的奇异性指数，即设备运行状态的特征值(In this research project, we use LabVIEW to do wavelet-transformation to the signal collected by sensors in order to get singularity index from the singular points of the particular signal’s envelope. Through comparing this eigenvalue to the figures in a standard database, we can know the functional status of the instruments. Not only the diagnoses are continuously displayed, but alarms about signs of dysfunction will also be raised in a proper way. In other words, both the supervision of the instrument and warning of dysfunction can be achieved)

二维双密度双树（复数）小波变换包。包含基于小波变换的基本的阈值处理运算。(2-D Double-Density Complex Wavelet Transform。There are two versions of the 2-D double-density dual-tree DWT: (1) The 2-D double-density dual-tree real-oriented DWT, which is 2-times expansive, and (2) The 2-D double-density dual-tree complex-oriented DWT, which is 4-times expansive.)