Description: Included are the files wav1.m, wav2.m, wavecoef.mat and readme.
wav2 function implements the tree structured wavelet transform of the input matrix, up to the given level of decomposition. Wav2 uses another function called wav1, which takes the well known wavelet transform of the given matrix. Daubechies wavelet coefficients are used for wavelet transform operation wahich is saved in wavcoeff.mat.
-Included are the files wav1.m, wav2.m. wavecoef.mat and readme. wav2 function biennium max the tree structured wavelet transform of t he input matrix. given up to the level of decomposition. Wav2 use s wav1 another function called, which takes the well known wavelet transform of the given matrix. Daubechies wavelet coeffici separations are used for wavelet transform operation w ahich is saved in wavcoeff.mat. Platform: |
Size: 4377 |
Author:yupenghui |
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Description: Matlab环境下利用Daubechies小波族中的db10小波对散斑退化图像进行了压缩数值仿真研究。研究表明：阈值以及分解层次的选取影响着图像压缩的质量。-Matlab environment using Daubechies wavelet tribal db10 wavelet the withdrawal of speckle image compression for the numerical simulation. Research shows : the threshold level of decomposition is influenced by the quality of image compression. Platform: |
Size: 4264 |
Author:安 |
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Description: 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。-through the design process to a simple one-dimensional signal with a Gaussian white noise after Daubech ies wavelet Morlet wavelet and Haar wavelet transform, and thus the wavelet coefficients; Decomposition again by changing the levels of high frequency coefficients of the wavelet reconstruction signal to eliminate noise purposes. In this program the process can intuitively understand wavelet decomposition process and the reconstruction of the Signal Noise Canceling the important role and the layers of high-frequency coefficients weight coefficient handling of the selection of signal denoising effects of. Platform: |
Size: 161420 |
Author:牛牛 |
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Description: Included are the files wav1.m, wav2.m, wavecoef.mat and readme.
wav2 function implements the tree structured wavelet transform of the input matrix, up to the given level of decomposition. Wav2 uses another function called wav1, which takes the well known wavelet transform of the given matrix. Daubechies wavelet coefficients are used for wavelet transform operation wahich is saved in wavcoeff.mat.
-Included are the files wav1.m, wav2.m. wavecoef.mat and readme. wav2 function biennium max the tree structured wavelet transform of t he input matrix. given up to the level of decomposition. Wav2 use s wav1 another function called, which takes the well known wavelet transform of the given matrix. Daubechies wavelet coeffici separations are used for wavelet transform operation w ahich is saved in wavcoeff.mat. Platform: |
Size: 4096 |
Author:yupenghui |
Hits:
Description: Matlab环境下利用Daubechies小波族中的db10小波对散斑退化图像进行了压缩数值仿真研究。研究表明：阈值以及分解层次的选取影响着图像压缩的质量。-Matlab environment using Daubechies wavelet tribal db10 wavelet the withdrawal of speckle image compression for the numerical simulation. Research shows : the threshold level of decomposition is influenced by the quality of image compression. Platform: |
Size: 4096 |
Author:安 |
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Description: 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。-through the design process to a simple one-dimensional signal with a Gaussian white noise after Daubech ies wavelet Morlet wavelet and Haar wavelet transform, and thus the wavelet coefficients; Decomposition again by changing the levels of high frequency coefficients of the wavelet reconstruction signal to eliminate noise purposes. In this program the process can intuitively understand wavelet decomposition process and the reconstruction of the Signal Noise Canceling the important role and the layers of high-frequency coefficients weight coefficient handling of the selection of signal denoising effects of. Platform: |
Size: 160768 |
Author:牛牛 |
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Description: 对二维信号（例如二维图像），实现多级小波的分解和重构，用到的小波函数是 DBN 小波,即Daubechies小波。-Of two-dimensional signal (such as two-dimensional image), the realization of multi-level wavelet decomposition and reconstruction, the wavelet function is used wavelet DBN, namely Daubechies wavelet. Platform: |
Size: 1024 |
Author:a fei |
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Description: 代码主要计算Daubechies紧支集滤波器系数L0_d和H1_d,然后采用这两个系数对信号进行小波分解-The main calculation code compactly supported Daubechies filter coefficients L0_d and H1_d, then the use of these two coefficients of wavelet decomposition signals Platform: |
Size: 1024 |
Author:熊子东 |
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Description: Digitized images have replaced analog images as photographs or x-rays in many different fields. In their raw form, digital images require a tremendous memory capacity for storage and large amount of bandwidth for transmission. In the last two decades, many researchers have been devoted to develop new techniques for image compression. More recently, wavelets have become a cutting edge technology for compressing the images by extracting only the visible elements. In this paper a wavelet based image decomposition algorithm has been implemented. Also, a nonuniform threshold technique based on average intensity values of pixels in each sub band has been proposed to remove the insignificant wavelet coefficients in the transformed image. Experimental results are obtained to compare the Daub2, Daub3 and Daub4 compactly supported (Daubechies) orthogonal wavelets on various test images using two important performance parameters – compression ratio and PSNR Platform: |
Size: 281600 |
Author:Sid |
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Description: 通过设计Visual C程序源码对简单易懂的一维信号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波与Haar小波变换，从而的到小波分解系数；再通过改变分解的到的各层高频系数数进行信号的小波重构达到消噪噪的目的。在这一程序源码实现的过程中能直观地理解信号小波分解重构的过程与在信号消噪中的重要作用，和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。 可直接
-Design Visual C program source code on a simple one-dimensional signal with a Gaussian white noise Daubechies wavelet, Morlet wavelet and Haar wavelet transform, and thus to the wavelet coefficients and then change the decomposition to each storey wavelet reconstruction frequency coefficient of the number of signal to noise canceling noise. In the process of realization of this program source code can be intuitively understood the signal wavelet decomposition and reconstruction process and an important role in signal denoising, and the selection coefficient in the high frequency coefficients of the layers of the weight of processing noise cancellation signal impact. Can be directly Platform: |
Size: 160768 |
Author:xlli |
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Description: 利用Daubechies小波(m=2)对函数的小波包分解与重构。把函数值作为起始系数-Use Daubechies wavelet (m = 2) the function of wavelet packet decomposition and reconstruction. The function value as initial coefficient Platform: |
Size: 1024 |
Author:申淑媛 |
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Description: y=d2wavelet(x,Fs,level) does the 2nd order Daubechies Wavelet Transform of signal x with a sampling frequency Fs and the DWT is decomposition is done upto a level
It returns the matrix of all decompositions and the final approximations.
Instead of using the matlab s inbuilt DWT function, this file explains the algorithm for DWT. Mostly useful for learning & academic purposes.
For other wavelets, the filter values alone can be changed or WFILTERS can
be used.
The function basically is for Condition Monitoring of rotating equipments by vibration based bearing fault diagnosis by the author.
Example:
clear all
t=[0:0.0003:8*pi]
x=sin(5000*t)+sin(1000*t)
x=x(1:2^16)
level=5 Fs=1/0.003
d2wavelet(x,Fs,level)
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-y=d2wavelet(x,Fs,level) does the 2nd order Daubechies Wavelet Transform of signal x with a sampling frequency Fs and the DWT is decomposition is done upto a level
It returns the matrix of all decompositions and the final approximations.
Instead of using the matlab s inbuilt DWT function, this file explains the algorithm for DWT. Mostly useful for learning & academic purposes.
For other wavelets, the filter values alone can be changed or WFILTERS can
be used.
The function basically is for Condition Monitoring of rotating equipments by vibration based bearing fault diagnosis by the author.
Example:
clear all
t=[0:0.0003:8*pi]
x=sin(5000*t)+sin(1000*t)
x=x(1:2^16)
level=5 Fs=1/0.003
d2wavelet(x,Fs,level)
Thanks for Downloading. Don t forget to rate or comment.
Platform: |
Size: 2048 |
Author:无界 |
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Description: 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. Platform: |
Size: 159744 |
Author:赵远洋 |
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