High quality non-blind image deconvolution using the Fields of Experts prior

被引：6

作者：

Chen, Jinwei ^{[1
]}

Dong, Wende ^{[1
]}

Feng, Huajun ^{[1
]}

Xu, Zhihai ^{[1
]}

Li, Qi ^{[1
]}

机构：

[1] Zhejiang Univ, State Key Lab Modern Opt Instrumentat, Hangzhou 310027, Zhejiang, Peoples R China

来源：

OPTIK | 2013年 / 124卷 / 18期

基金：

中国国家自然科学基金;

关键词：

Non-blind image deconvolution; Bayesian probabilistic framework; Fields of Experts (FoE) prior; ALGORITHM;

D O I：

10.1016/j.ijleo.2012.11.004

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this paper, we propose a non-blind image deconvolution method under Bayesian probabilistic framework. A robust prior, i.e., the Fields of Experts (FoE) prior which is learned from natural images is adopted to regularize the latent image. To solve the resulted optimization problem, the Split Bregman algorithm is utilized. We use a data set to verify the effectiveness of the proposed approach. Experimental results show that the restored images are of higher quality than that of some state of the art algorithms. (C) 2012 Elsevier GmbH. All rights reserved.

引用

页码：3601 / 3606

页数：6

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