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wavelet_kompressor
- Wavelet compression algorithm. Start by computing a complete wavelet-transform using the cohen-daubechies-fevereau (2,2) biorthogonal wavelet (which incidentally seems to be the same as used for lossless JPEG2000, but they call it a (5,3)?).
DWT_DaubLen4
- Daubechies小波变换，内含Daubechies 4滤波器生成函数，以及小波正、反变换函数-Daubechies wavelet transform, Daubechies 4 filter containing the generating function, n and wavelet inverse transform function
Wavelet_waterprint_process
- 在图像中加入水印，使用2维离散Daubechies小波变换在图像中加入水印，重构图像，通过检测阈值，将阈值对应水印分离。-Add watermark in the image, using two-dimensional discrete wavelet transform Daubechies watermark in the image, the reconstructed image, through the detection threshold, the threshold corre
Haar-wavelet
- 与标准的傅里叶变换相比，小波分析中使用到的小波函数具有不唯一性，即小波函数 具有多样性。小波分析在工程应用中，一个十分重要的问题就是最优小波基的选择问题，因为用不同的小波基分析同一个问题会产生不同的结果。目前我们主要是通过用小波分析方法处理信号的结果与理论结果的误差来判定小波基的好坏，由此决定小波基。常用小波基有Haar小波、Daubechies(dbN)小波、Mexican Hat(mexh)小波、Morlet小波、Meyer小波等5种。-Compared with the standard
wavelet
- With the development of power systems and the extensive application of nonlinear elements in the electricity system, the problem of ferroresonance do harm to the network and electricity facilities has become more prominent. Wavelet analysis h
Wavelet-dec--rec
- 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
V1I7-0028
- Performance analysis of steganography is done for selecting better wavelet for application by analyzing Peak Signal Noise Ratio(PSNR) of different wavelet families like Haar, Daubechies, Biorthogonal, Reverse Biorthogonal & Meyer wavelet(dmey) on res
daubechies
- there is wavelet transform, called daubichies transform
DWT
- Discrete Wavelet Transform using Daubechies Wavelet family for Stationery and non-stationery signal and test with ECG signal for compression as well. The Matlab code are very simple and detailed one to understand easily. Can change code and produce o
image-segmentation
- In this paper, the concepts of Daubechies wavelet transform are discussed. The Daubechies wavelets are a family of orthogonal wavelets defining a discrete wavelet transform and characterized by a maximal number of vanishing moments for some g
5
- 给出五种常用小波基的时域和频域波形图,常用小波基有Haar小波、Daubechies(dbN)小波、Mexican Hat(mexh)小波、Morlet小波、Meyer小波等5种。-The time domain and frequency domain waveform diagram of five kinds of wavelet bases, wavelet base is Haar wavelet, Daubechies wavelet, Mexican (dbN) Hat (mexh
wavelet
- 2-D discrete 8-point Daubechies wavelet.
xiaobobianhuan
- 一维小波变换，基于Mallat算法编写，用Daubechies正交小波基作为卷积核对输入信号作卷积，对结果进行重排可得一维小波变换后的尺度系数和小波系数。-one dimensional wavelet transform based on Mallat algorithm is written, with Daubechies orthogonal wavelet base as a convolution convolution check input signal, the results
Image-Representation
- 文章主要提出了一种基于Gobar 小波变换的图像表示与描述方法。-This paper extends to two dimensions the frame criterion developed by Daubechies for one-dimensional wavelets, and it computes the frame bounds for the particular case of 2D Gabor wavelets.
waveletfilter
- Get Daubechies Wavelet of order N
小波变换
- 用keil4编写C代码实现小波变换，以Daubechies小波基进行常用波形的尺度系数和小波系数提取(C code written in keil4, wavelet transform, Daubechies wavelet basis for the commonly used waveform scale coefficients and wavelet coefficients extraction)
untitled5
- This article presents a new method for identification and classification of faults based on wavelet multiresolution analysis (MRA). Daubechies eight (D-8) wavelet transforms of the three phase currents on a transmission line are used.
Daubechies
- genetic algorith help you
Ten_Lectures_On_Wavelets
- 本书包含10个讲座由Daubechies博士为在小波及其应用1990 cbms-nsf会议主讲人。小波是一种数学发展，许多专家认为它可能会彻底改变信息存储和检索的世界。它们是一种相当简单的数学工具，现在被应用于压缩数据，如指纹、气象卫星照片和医用X射线。以前人们认为这些数据在不丢失关键细节的情况下是不可能压缩的。(This monograph contains 10 lectures presented by Dr. Daubechies as the principal speaker at
小波十讲 中文版 Ingrid Daubechies著 李建平译
- 小波十讲，很经典的资料，。。。。。。。。。。。。。。。(Wavelet ten, very classic data)