A New Blind Deblurring Method via Hyper-Laplacian Prior

被引：6
|
作者：
Kong, Jun [1 ,2 ]
Lu, Kesai [1 ]
Jiang, Min [1 ]
机构：
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, 1800 Lihu Ave, Wuxi 214122, Peoples R China
[2] Xinjiang Univ, Coll Elect Engn, 14 Shengli Rd, Urumqi 830046, Peoples R China
来源：
ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY | 2017年 / 107卷
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Blind deblurring; hyper-Laplacian prior; generalized soft thresholding; ALGORITHM;
D O I：
10.1016/j.procs.2017.03.170
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Blind image deblurring is always a thorny problem due to its uncertainty. In this manuscript, we proposed a new blind deblurring method in the maximum a posterior (MAP) framework to remove the blur of the image. Firstly, we choose the hyper-Laplacian prior to be a regularization of the gradients of an image. Secondly, we adopt an operator called generalized soft thresholding (GST) to solve the non-convex problem during the whole deblurring process. Thirdly, we compared our method with some popular approaches in both quantitative and qualitative aspects. Experimental results show that the method proposed by us performs much better than the other approaches.
引用
收藏
页码：789 / 795
页数：7
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