Image Denoising Using Gaussian Scale Mixtures in Lifting Stationary Wavelet Coefficient

被引：0

作者：

Zhang, Dongmei ^{[1
]}

Wang, Minzhi ^{[1
]}

Liu, Jianhua ^{[2
]}

Xiao, Zhong ^{[3
]}

机构：

[1] Zhangjiakou Educ Coll, Dept Sci, Zhangjiakou 075000, Peoples R China

[2] Donghua Univ, Coll Mech Engn, Shanghai 200000, Peoples R China

[3] Shijiazhuang Tiedao Univ, Shijiazhuang 050043, Peoples R China

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ELECTRONIC & MECHANICAL ENGINEERING AND INFORMATION TECHNOLOGY (EMEIT-2012) | 2012年 / 23卷

关键词：

Lifting stationary wavelet transform; GSM; BLS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new method for image denoising is proposed in this paper. Firstly, apply the Lifting Stationary Wavelet transform on the denoised image. Secondly, Gaussian scale mixtures (GSM) is combined with the marginal distributions of neighbor coefficients in the nonsub-sampled contourlet domain are modeled. The Bayes least square estimation is adopted to evaluate high pass coefficient to remove additive white Gaussian noise. Finally, inverse Lifting Stationary Wavelet transform is applied on the denoised coefficients to reconstruction the denoised image.

引用

页数：4

共 9 条

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