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Image Denoising with Trivariate Shrinkage Based on Sharp Frequency Localization Contourlet

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成果类型:
期刊论文
作者:
Chi, Zhang;Huixia, Jin
通讯作者:
Huixia, Jin(jinhuixia2002@163.com)
作者机构:
[Chi, Zhang] Department of Information Science and Engineering, Hunan City University, Yiyang, 413000, China
[Huixia, Jin] Department of Communication and Electronic Engineering, Hunan City University, Yiyang, 413000, China
通讯机构:
Department of Communication and Electronic Engineering, Hunan City University, Yiyang, China
语种:
英文
关键词:
Bayesian;contourlet;image denoising;statistical distribution;shrinkage function;trivariate distribution.
期刊:
Recent Advances in Electrical & Electronic Engineering
ISSN:
2352-0965
年:
2016
卷:
9
期:
1
页码:
6-10
机构署名:
本校为第一且通讯机构
院系归属:
信息与电子工程学院
摘要:
Conventional thresholding shrinkage for denoising is designed with assumption that the coefficients in transformation domain are independent. However, in practice, natural images’ coefficients in transformation domain have significant dependencies. In this paper, we proposed a novel method for image denoising by exploring the dependencies among the coefficients. The method considered three corresponding coefficients, including the noisy coefficient, its parent coefficient and its neighbor coefficient based on the Sharp Frequency Localization C...

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