Description: 现在在所有图像去噪滤波中最较理想的去噪算法——Bayesian Least Squares - Gaussian Scale Mixture的matlab实现代码，应用丰富，与商用图像处理软件媲美，其原理可以参见J Portilla, V Strela, M Wainwright, E P Simoncelli, \"Image Denoising
using Scale Mixtures of Gaussians in the Wavelet Domain Platform: |
Size: 1370442 |
Author:pudndcy |
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Description: 现在在所有图像去噪滤波中最较理想的去噪算法——Bayesian Least Squares - Gaussian Scale Mixture的matlab实现代码，应用丰富，与商用图像处理软件媲美，其原理可以参见J Portilla, V Strela, M Wainwright, E P Simoncelli, "Image Denoising
using Scale Mixtures of Gaussians in the Wavelet Domain-Now image denoising filter in all the most better denoising algorithm- Bayesian Least Squares- Gaussian Scale Mixture realize the matlab code, application-rich, with the commercial image processing software rival, and its principle can be found in J Portilla, V Strela, M Wainwright, EP Simoncelli, Platform: |
Size: 1370112 |
Author:pudndcy |
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Description: 该仿真算法实现了论文Image Denoising
using Scale Mixtures of Gaussians in the Wavelet Domain中图像降噪-This Algorithm utilize the method of paper "Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain " Platform: |
Size: 1371136 |
Author:Alex |
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Description:
BLS-GSM代表“Bayesian Least Squares - Gaussian Scale Mixture(贝叶斯最小二乘-高斯尺度混合模型)”。
这个工具箱实现了该篇论文中介绍的算法：
J Portilla, V Strela, M Wainwright, E P Simoncelli, Image Denoising
using Scale Mixtures of Gaussians in the Wavelet Domain, IEEE
Transactions on Image Processing. vol 12, no. 11, pp. 1338-1351,
November 2003
这个工具箱进行图像去噪时，假定噪声类型为高斯噪声并且我们知道它的功率谱密度（不需要是白色的）。-BLS-GSM stands for Bayesian Least Squares- Gaussian Scale Mixture .
This toolbox implements the algorithm described in:
J Portilla, V Strela, M Wainwright, E P Simoncelli, Image Denoising
using Scale Mixtures of Gaussians in the Wavelet Domain, IEEE
Transactions on Image Processing. vol 12, no. 11, pp. 1338-1351,
November 2003
This
tool implements image denoising assuming that the noise is Gaussian,
and that we know its power spectral density (it does not need to be white). Platform: |
Size: 999424 |
Author:沙天飞 |
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Search - scale mixtures of Gaussians in the wavelet domain