Student's-t Mixture Model Based Image Denoising Method with Gradient Fidelity Term

被引：0
|
作者：
Zhang, J. W. [1 ]
Liu, J. [1 ]
Zheng, Y. H. [2 ]
Wang, J. [2 ]
机构：
[1] Nanjing Univ Informat Sci & Technol, Coll Math & Stat, Nanjing 210044, Jiangsu, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, CICAEET, Coll Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
来源：
ADVANCES IN COMPUTER SCIENCE AND UBIQUITOUS COMPUTING | 2017年 / 421卷
关键词：
Image denoising; Student's-t mixture model; Structured sparse representation; Gradient fidelity term;
D O I：
10.1007/978-981-10-3023-9_49
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The mixture models based structured sparse representation (MM-SSR) method has received much attention in recent years. Especially, the student's-t mixture model based structured sparse representation (SMM-SSR) has been widely used due to the fact that it has a heavy tail and is robust to noise. In this paper, for further enhancing the performance of SMM-SSR, we attempt to incorporate the gradient fidelity term with the student's-t mixture model for image denoising. Experiment results show that our proposed method outperforms the traditional SMM-SSR method.
引用
收藏
页码：306 / 311
页数：6
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