Description: 这个matlab程序用于实现广义高斯分布的参数估计，非常有用-Matlab program for the realization of the generalized Gaussian distribution of parameter estimation, a very useful Platform: |
Size: 7168 |
Author:吴 |
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Description: 自己编写的matlab程序，用于广义高斯分布的形状参数估计。-Matlab have written procedures for the generalized Gaussian distribution shape parameter estimation. Platform: |
Size: 132096 |
Author:lpmsu |
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Description: 压缩包中的算法使用moment matching和MLE估计矩阵的广义高斯分布参数，推荐一下。-Compression package using the moment matching algorithms and the estimated matrix of MLE parameters of generalized Gaussian distribution, recommend. Platform: |
Size: 7168 |
Author:Ming |
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Description: 基于复数小波域广义高斯分布模型的纹理图像检索。
-Complex Wavelet Domain Based on Generalized Gaussian Distribution Model for Texture Image Retrieval. Platform: |
Size: 1045504 |
Author:爱学习 |
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Description: 对广义高斯分布有个基本的认识，为以后的应用打下基础-The generalized Gaussian distribution have a basic understanding of lay the foundation for future applications Platform: |
Size: 132096 |
Author:zhangyan |
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Description: 提出了基于Contourlet变换的数字图像水印算法。与小波变换不同的是，Contourlet变换采用类似于线段
（contour segment）的基得到一种多分辨、局部化、方向性的图像表示。水印信号通过基于内容的乘性方案加载
到 Contourlet 变换系数。在采用零均值广义高斯分布拟合 Contourlet 变换系数的基础上，提出采用极大似然估计
实现水印的盲检测。依据 Neyman-Pearson 准则，在给定虚警率的情况下对判决准则进行了优化。实验结果表明
在保证水印隐蔽性的前提下，水印对常见的信号处理手段以及几何变换具有很好的稳健性-Contourlet transform is proposed based on digital image watermarking algorithm. The difference is that with the wavelet transform, Contourlet transformation similar to that of line (contour segment) of the base by a multi-resolution, localization, directional image representation. Watermark-based content of the program is loaded into Contourlet by transform coefficients. The introduction of zero-mean generalized Gaussian distribution fitting Contourlet transform coefficient based on maximum likelihood estimation proposed by the blind watermark detection. Based on Neyman-Pearson criterion, the false alarm rate in a given case to the sentencing guidelines were optimized. The results show that the watermark hidden in the guarantee under the premise of the watermark signal processing means of the common geometric transformations and is robust Platform: |
Size: 134144 |
Author:betty |
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Description: 一种广义高斯分布的参数快速估计法,实现GGD函数拟合-The parameters of a generalized Gaussian distribution fast estimation method to achieve function fitting GGD Platform: |
Size: 308224 |
Author:li820325 |
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Description: 广义高斯分布随机样本的产生，具体讲解了如何产生广义高斯分布随机样本。-Generalized gaussian distribution of random samples produced to explain in detail how to produce, the generalized gaussian random samples. Platform: |
Size: 1024 |
Author:shenlei |
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Description: 仿真产生十种概率分布的随机序列，包括均匀分布、高斯分布、指数分布、广义指数分布、混合指数分布、韦布尔分布、瑞利分布
广义瑞利分布、拉普拉斯分布、柯西分布等，并进行参数检验，概率分布检验和独立性检验-Simulation produces ten kinds of probability distribution of random sequences, including uniform, Gaussian, exponential, generalized exponential distribution, mixed exponential distribution, Weibull distribution, Rayleigh distribution of generalized Rayleigh distribution, the Laplace distribution, Cauchy distribution etc., and parametric tests, the probability distribution of test and independence test Platform: |
Size: 2048 |
Author:高双成 |
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Description: 这个是广义高斯分布随机数产生程序，它可以用做广义高斯分布形状参数的估计，以及用在图像处理，独立成分分析，信号处理，数字水印等方面。-This is the random numbers about the generalized Gaussian distribution.It can be used in image processing, ICA, signal processing,digital watermark and so on. Platform: |
Size: 6144 |
Author:飞鱼 |
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Description: 首先通过Gaussian White Noise.vi产生一个满足高斯分布的随机数序列，然后通过Create Histogram和Statistic两个Express VI对该随机序列进行分析。
-First produced by Gaussian White Noise.vi meet a Gaussian distribution of random numbers, then Create Histogram and Statistic Express VI to the two random sequence analysis. Platform: |
Size: 10240 |
Author:znn |
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Description: 这是几个不同形状参数的广义高斯分布的密度函数作图，以及形状参数的极大似然估计的程序。-This is several different shape parameters of the generalized Gaussian distribution density function mapping, as well as the shape parameter of the great likelihood estimation procedures. Platform: |
Size: 1024 |
Author:飞鱼 |
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Description: denoise
In BayesShrink[5] we determine the threshold for
each subband assuming a Generalized Gaussian
Distribution(GGD) . The GGD is given by
GG¾ X ¯ (x) = C(¾ X ¯ )exp¡ [® (¾ X ¯ )jxj]¯ (6)
¡ 1 < x < 1 ¯ > 0, where
® (¾ X ¯ ) = ¾ ¡ 1
X [ ¡ (3=¯ )
¡ (1=¯ ) ]1=2
and
C(¾ X ¯ ) = ¯ ¢ ® (¾ X ¯ )
2¡ ( 1
¯ )
and ¡ (t) =
R1
0 e¡ uut¡ 1du.
The parameter ¾ X is the standard deviation and ¯
is the shape parameter It has been observed[5] that
with a shape parameter ¯ ranging from 0.5 to 1, we
can describe the the distribution of coefficients in a
subband for a large set of natural images.Assuming
such a distribution for the wavelet coefficients, we empirically
estimate ¯ and ¾ X for each subband and try
to find the threshold T which minimizes the Bayesian
Risk, i.e, the expected value of the mean square error.-denoise
In BayesShrink[5] we determine the threshold for
each subband assuming a Generalized Gaussian
Distribution(GGD) . The GGD is given by
GG¾ X ¯ (x) = C(¾ X ¯ )exp¡ [® (¾ X ¯ )jxj]¯ (6)
¡ 1 < x < 1 ¯ > 0, where
® (¾ X ¯ ) = ¾ ¡ 1
X [ ¡ (3=¯ )
¡ (1=¯ ) ]1=2
and
C(¾ X ¯ ) = ¯ ¢ ® (¾ X ¯ )
2¡ ( 1
¯ )
and ¡ (t) =
R1
0 e¡ uut¡ 1du.
The parameter ¾ X is the standard deviation and ¯
is the shape parameter It has been observed[5] that
with a shape parameter ¯ ranging from 0.5 to 1, we
can describe the the distribution of coefficients in a
subband for a large set of natural images.Assuming
such a distribution for the wavelet coefficients, we empirically
estimate ¯ and ¾ X for each subband and try
to find the threshold T which minimizes the Bayesian
Risk, i.e, the expected value of the mean square error. Platform: |
Size: 13753344 |
Author:mik |
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Description: 广义高斯概率分布函数的指数估计，包括矩估计和最大似然然估计，并对这两种估计进行了比较，已通过测试。
-Generalized Gaussian probability distribution function of the index estimates, including the moment estimation and maximum likelihood estimation, and the two estimates were compared, has been tested. Platform: |
Size: 9216 |
Author:ywhfdl |
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Description: 我按这篇文章做的，广义高斯分布及其在图像去噪中的应用，没有完全做出来，谁做出来了，分享一下- The statistics of imagewavelet coefficients is non2Gaussian and can be described by generalized Gaussian
distribution (GGD). The paper investigates the issues of GGD statisticalmodel for wavelet coefficients in a sub2
band and the corresponding parameters estimation . A Bayesian estimate based subband adaptive thresholding de2
noising algorithm is designed, and the performance of noise reduction of this algorithm, compared with other clas2
sicalwavelet thresholding denoising methods, is discussed according to simulation results .
Keywords image wavelet coefficients generalized Gaussian distribution BayesShrink algorithm image de Platform: |
Size: 342016 |
Author:李胖子 |
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Description: 运动检测matlab程序和广义高斯分布的matlab程序-Motion detection matlab and generalized gaussian distribution of the matlab program Platform: |
Size: 6144 |
Author:mfsv$566651 |
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Description: 运动检测matlab程序和广义高斯分布的matlab程序(Motion detection matlab and generalized gaussian distribution of the matlab program) Platform: |
Size: 5120 |
Author:Ltuhtr
|
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Search - Generalized Gaussian Distribution