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: 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: 基于小波变换的图像融合的设计主要可分为三个模块，即图像的小波变换模块、小波系数的融合模块和图像的小波重构模块。
在图像的小波变换部分，主要研究小波的构造。小波的种类有很多，此设计中实现了基于Haar小波、Daubechies小波、Symlets小波和Coiflets小波的图像融合。-Based on wavelet transform image fusion of design can be divided into three modules, namely image wavelet transform module, integration module wavelet coefficients of the wavelet reconstruction and image modules. Wavelet transform in the image of the main research Wavelets. There are many types of wavelet, the realization of this design based on the Haar wavelet, Daubechies wavelet, Symlets wavelet and wavelet image fusion Coiflets. Platform: |
Size: 1883136 |
Author:lidan |
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Description: 是scilab的源程序，是一维的haar小波变换和一维的daubechies小波变换。已经调试过，可以使用。里面还附有卷积的源代码。-Is the scilab source is one-dimensional haar wavelet transform and one-dimensional wavelet transform daubechies. Have been debug, you can use. There was also accompanied by convolution of the source code. Platform: |
Size: 402432 |
Author:王静 |
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Description: 执行一维和二维小波变换在MATLAB环境中。十几包括的小波函数有：
* Haar
* Daubechies 1-6
* Symlets 1-6
* Coiflets 1 and 2
* Splines and reverse splines
* CDF 9/7 and Le Gall 5/3
* S+P wavelets (2,2), (4,2), (4,4), (6,2), and (2+2,2)
* Two Ten "TT"
* Low-complexity design
* HVS design Visual 9/3
-Description Perform 1D and 2D wavelet transforms in MATLAB. WAVELET(W,L,X) computes the L-stage discrete wavelet transform (DWT) of signal X using wavelet W. For the inverse transform, WAVELET(W,-L,X) inverts L stages. Included wavelets are
* Haar
* Daubechies 1-6
* Symlets 1-6
* Coiflets 1 and 2
* Splines and reverse splines
* CDF 9/7 and Le Gall 5/3
* S+P wavelets (2,2), (4,2), (4,4), (6,2), and (2+2,2)
* Two Ten "TT"
* Low-complexity design
* HVS design Visual 9/3 Platform: |
Size: 10240 |
Author:chen huayi |
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Description: 1 Haar Wavelets
1.1 The Haar transform
1.2 Conservation and compaction of energy
1.3 Haar wavelets
1.4 Multiresolution analysis
1.5 Compression of audio signals
1.6 Removing noise from audio signals
1.7 Notes and references
2 Daub echies wavelets
2.1 The Daub4 wavelets
2.2 Conservation and compaction of energy
2.3 Other Daubechies wavelets
2.4 Compression of audio signals
2.5 Quantization, entropy, and compression
2.6 Denoising audio signals
2.7 Two-dimensional wavelet transforms
2.8 Compression of images
2.9 Fingerprint compression
2.10 Denoising images
2.11 Some topics in image processing
2.12 Notes and references
3 Frequency analysis
3.1 Discrete Fourier analysis
3.2 Definition of the DFT and its properties
3.3 Frequency description of wavelet analysis
3.4 Correlation and feature detection
3.5 Object detection in 2D images
3.6 Creating scaling signals and wavelets
3.7 Notes and references Platform: |
Size: 4108288 |
Author:Rakesh |
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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: 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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Description: 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, Morlet wavelet, Meyer wavelet and 5.
Platform: |
Size: 59392 |
Author:叶斌 |
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