Ten.Lectures.on.Wavelets

小波变换 小波变换 (Wavelet Transform Wavelet Transform)

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)

小波神经网络实现短期电力负荷预测，神经网络模型及其BP学习算法。 (Wavelet neural network short term load forecasting, neural network model and BP learning algorithm.)

使用小波包分析信号的MATLAB程序，对初学者有用~~~~·( analysis Signal by Wavelet packet..matlab~~)

本文通过比较第一代和第二代小波算法特点,引入二代小波提升结构的概念,提出了一种基于二代小波提升结构的快速图像边缘检测算法 对三次B样条小波基实现提升格式,通过计算大尺度下分解子图的模值和幅角来确定边缘 经过实验比较,能比经典的边缘检测算法得出更精确的边缘图像,同时通过与基于第一代小波算法的边缘检测比较,基于二代小波提升格式的边缘检测算法计算更快速,更高效。(In this paper, by comparing first-and second-generation wavelet algorithm features, the introduction of second generation wavelet to enhance the concept of structure, a second generation wavelet-based structure to enhance the rapid image edge detection algorithm on the three B-spline wavelet lifting scheme to achieve through Calculation of large-scale mold subgraph decomposition value and increase the edge angle to determine the experimental comparison, than the classical edge detection algorithm to draw a more accurate edge image, at the same time with the first generation of wavelet-based edge detection algorithm comparison, based on the Second generation wavelet lifting scheme edge detection algorithm is faster and more efficient.)

一篇关于小波变换和希尔伯特变换的文章，处理的是无功功率。小波变换代码和希尔伯特变化代码(A Wavelet transform and Hilbert transform article, dealing with reactive power.Wavelet transform and Hilbert transform code)

实现db6小波提升变换，对信号进行5层分解和重构。另外被屏蔽的部分为离散小波变换。(Enhance the realization of wavelet transform db6, 5 layers of signal decomposition and reconstruction. In addition to being part of shielding DWT.)

这是本人的图像处理作业 包括利用各种基于小波的去噪过程，如硬阈值 软阈值等。。内容很丰富哦！相信一定对你有帮助的！(This is my image processing operations including the use of wavelet-based de-noising process, such as a hard threshold, such as soft-thresholding. . Oh, the content is very rich! I am sure will help you!)

Fast wavelet transforms and numerical algorithms 1

二代小波harr变换整数小波变换，适合图像处理专业人员参考(Second generation wavelet transform Harr integer wavelet transform for image processing professionals reference)