Image Denoising Using Bandelets and Hidden Markov Tree Models

被引：0
|
作者：
Zhang Wenge [1 ,2 ]
Wang Suang [2 ,3 ]
Liu Fang [1 ,2 ]
Gao Xinbo [2 ]
Jiao Licheng [2 ,3 ]
机构：
[1] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China
[2] Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Beijing, Peoples R China
[3] Xidian Univ, Inst Intelligent Informat Proc, Xian 710071, Peoples R China
来源：
CHINESE JOURNAL OF ELECTRONICS | 2010年 / 19卷 / 04期
基金：
新加坡国家研究基金会; 中国国家自然科学基金;
关键词：
Image denoising; Bandelets; HMT model; Image modeling; Statistics models; Multiscale geometry analysis;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, both the marginal and joint statistics of second generation Orthogonal bandelet transform (OBT) coefficients of natural images are firstly studied, and the highly non-Gaussian marginal statistics and strong interscale, interlocation and interdirection dependencies among OBT coefficients are found Then a Hidden Markov tree (HMT) model in OBT domain which can effectively capture all dependencies across scales, locations and directions is developed The main contribution of this paper is that it exploits the edge direction information of OBT coefficients, and proposes an image denoising algorithm (B-HMT) based on HMT model in OBT domain We apply B-HMT to denoise natural images which contaminated by additive Gaussian white noise, and experimental results show that B-HMT outperforms the Wavelet HMT (W-HMT) and Contourlet HMT (C-HMT) in terms of visual effect and objective evaluation criteria
引用
收藏
页码：646 / 650
页数：5
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