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WaveletVC++Res
- 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 Ga
fjxhfj
- 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.
Generatednoisypolynomialsignal
- 生成含噪的多项式信号一维小波分解重构第1-4层逼近系数-Noisy polynomial signal is generated one-dimensional wavelet reconstruction of the first approximation factor of 1-4 layers
wavelet
- 为了避免常规正交小波变换所引起的图像边缘失真，特别采用了双正交小波变换进行图像 的多分辨率分解。在融合处理时，为了获得更丰富的细节和更好的融合效果，对不同分解层、不 同方向上的高频分量以及同一分解层图像中的不同区域均采用不同的融合算子。实验结果表明， 该融合方法和融合规则是十分有效的，融合后图像的边缘和纹理细节都更加突出-In order to avoid the conventional orthogonal wavelet transform image edge distort
waveletDB4PRO
- 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进行小波分解系数；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-Understand the discrete wavelet transform in the fundamental principles and algorithms, based on the VC program by designing a simple one-dimensional signal wavelet coef
wavelet_1D
- 二进小波 分解,可以调节变化层数，可以自己编写下采样函数-Dyadic wavelet decomposition, changes in number of layers can be adjusted, you can write your own down-sampling function
wavelet-matlab
- 基于小变换，以及边缘融合算法的图像边缘检测算法。包含了在多尺度下层层检测分解重构图像-Based transformations, and the edge of fusion algorithm for image edge detection algorithm. Contains layers of detection in multi-scale decomposition of the reconstructed image
remove-noise-by-wavelet-transform
- 小波去噪,通过对含噪信号进行四层小波分解，此处利用db3小波系列，通过仿真发现可以很好的消除信号中的噪声-Wavelet denoising, the signal through four layers of noisy wavelet decomposition, where the use db3 wavelet series, the simulation found in the well to eliminate the noise signal
tuxiangfenjie
- 小波变换，用于图像分解，使图像分为几层，并分析每层的信号。-Wavelet transform for image decomposition, the image is divided into several layers, and analyze each of the signal.
li6-3
- 给定一信号，用db1对信号分别进行单尺度和三尺度分解，求出各层分解的低频系数和高频系数，并分别对低频信号和高频信号进行重构，-Given a signal, the signal separately with db1-scale and three single-scale decomposition, the decomposition of low-frequency coefficients obtained layers and high-frequency coefficients,
wavelet_wx
- （1.）二维信号的小波分解。选择一个小波和小波分解的层次N，然后计算信号s到第N层的分解。 （2）对高斯系数进行阈值量化。对于从1到N的每一层，选择一个阈值，并对这一层的高斯系数进行软阈值量化处理。 （3）二维信号的重构。根据小波分解的第N层的低频系数和经过修改的从第1层到第N层的各层高频系数计算二维信号的小波重构。 -(1) 2 d signal wavelet decomposition. Choose a wavelet and wavelet decomposition le
UWT
- 这是一个对图像做平稳Haar小波变换的程序，其中分解层数可以自由设置。-This is a Haar image to do the stationary wavelet transform procedure, in which the decomposition layers can be freely set.
Wavelets
- 小波数字图像处理，小波（1~3层图像处理），行变换，列变换，滤波（高通、低通）。图像增强，融合等-Wavelet digital image processing, wavelet (1 to 3 layers of image processing), row transform and column transform, filter (high pass, low pass). Image enhancement, integration, etc.
PCA_Nicolas
- 提出一种基于主成分分析(PCA)分解的图像融合框架。对源图像进行主成分分析,依据前几个主成分重建图像,经过下采样过程得到近似图像,对近似图像进行上采样,得到上层图像的差异图像(即细节图像),将最底层近似图像与各层细节图像进行累加完成图像的重构-The proposed fusion framework based on principal component analysis (PCA) decomposition. Principal component analysis of the sou
wavelet_extend
- 小波变换各层各级分解效果演示，吐血整理，有注释说明。-Wavelet transform decomposition levels results presentation layers, vomiting finishing, annotated.
SE
- 小波分解层数为5层，提取高频系数，进行单只重构，从而得到各组系数-Wavelet decomposition level for five layers, extracting high-frequency coefficients, only a single reconstruction, resulting in each set of coefficients
dwtmtx
- 小波变换矩阵生成 代码主要实现了2D-DWT的矩阵生成，只需输入维度以及分解层数即可生成相应的小波变换矩阵-Wavelet transform matrix generating code mainly realize the 2D-DWT matrix generation, simply enter the dimensions and number of layers to generate the corresponding decomposition wavelet transform
wavelet
- 对雾天图像利用小波变换分解，实现图像去雾增强，分解的层数越多，处理后的效果越明显。-On the fog image using wavelet transform decomposition, to achieve the image to fog enhancement, decomposition of the number of layers, the more obvious the effect of the treatment.
xiaobo
- 利用小波变换，通过设置阈值对图像进行去噪。 首先对图像用小波进行2层分解，然后通过设置硬阈值和软阈值的方法，对图像高频部分去噪。(The wavelet transform is used to denoise the image by setting threshold. Firstly, the image is decomposed into 2 layers by wavelet, and then the high frequency part of the image is d