NON-BLIND IMAGE RESTORATION WITH SYMMETRIC GENERALIZED PARETO PRIORS

被引:0
作者
Mei, Xing [1 ,2 ]
Hu, Bao-Gang [2 ]
Lyu, Siwei [1 ]
机构
[1] SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA
[2] Chinese Acad Sci, NLPR, Beijing 100864, Peoples R China
来源
2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2014年
关键词
Symmetric Generalized Pareto; Half-Quadratic Splitting; SCALE MIXTURES; GAUSSIANS; ALGORITHM;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper presents a new non-blind image restoration method based on the symmetric generalized Pareto (SGP) prior, which models the heavy-tailed distributions of gradients for natural images. Through experiments we show that the SGP model achieves log likelihood scores comparable to the hyper-Laplacian model when fitted to gradients and other band-pass filter responses. More importantly, when incorporated into a Bayesian MAP framework for non-blind image restoration, the SGP model leads to a closed-form solution for a per-pixel subproblem, which affords computational advantages in comparison with the numerical solutions induced from the hyper-Laplacian model. Experimental results show that our method is comparable to existing methods in restoration quality and processing speed.
引用
收藏
页码:4477 / 4481
页数:5
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