小波包分析

对图像做二维离散小波变换，分解级数为3级，并重构图像，不直接调用Matlab的dwt和idwt函数。(pair of 2-dimensional images DWT, decomposition of the three series, and Image Reconstruction, not directly call Matlab dwt and idwt function.)

This is a wavelet program.

An Efficient Randomized Algorithm for Detecting Circles

在理解了离散小波变换的基本原理和算法的基础上，通过设计VC程序对简单的一维信 号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波和Haar小波变换，从而得到小波分解系数；再通过改变分解得到的各层高频系数进行信号的小波重构达到消噪的目的。在这一程序实现的过程中能直观地理解信号小波分解重构的过程和在信号消噪中的重要作用，以及在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。(In understanding the basis of discrete wavelet transform basic principles and algorithms, through the design VC program Daubechies wavelet, Morlet wavelet and Haar wavelet transform after a simple one-dimensional signal plus a Gaussian white noise, resulting wavelet coefficients wavelet reconstruction signal again by changing the layers to achieve high-frequency coefficients decomposed noise cancellation purposes. Can intuitively understand wavelet decomposition and reconstruction process and an important role in signal de-noising in the process of implementation of this program, and in the high-frequency coefficients when the weight of the layers selected for signal processing coefficients denoising affected.)

利用小波变换实现和直方图均衡化相对比的图像的增强与分割.应该比较有用.分割也采取了几种技术的对比(using wavelet transform and histogram equalization relative ratio of image enhancement and segmentation. Be more useful. Pm cutting has taken several technical contrast)

小波分解在图像处理和电能质量分析中应用广泛，本文应用小波分解的方法将高频的暂态信号提取出来，获取暂态信号的特征量，就能够有效地计算暂态信号。(Wavelet decomposition in image processing and power quality analysis is widely used, this application of wavelet decomposition method to extract high-frequency transient signals, to obtain the amount of transient characteristics of the signal, we can effectively calculate the transient signal.)

实现构造小波基，求解小波尺度函数离散整数点的值以及实现尺度函数经伸缩后的离散值等功能。(Implement the constructor wavelet basis functions to solve the discrete wavelet scale and the realization of the value of an integer point scale after scaling function by a discrete value functions.)

基于小波变换的图片重建 有完整代码 工程 以及说明 涉及到图片的切割(image reconstruction based on wavelet translate )

利用curvelet_toolbox开发的一个红外和可见光图像融合代码。(The development of a curvelet_toolbox infrared and visible image fusion code.)

对输入的二维灰值图像，先进行提升Haar小波变换，再用经典的EZW算法对小波系数进行压缩，然后反变换重构原图像。(On the importation of two-dimensional gray value of images, first upgrade the Haar wavelet transform, and then classical EZW algorithm to compress the wavelet coefficients, and then inverse transform reconstruction of the original image.)