ON ALGORITHMS FOR AUTOMATIC DEBLURRING FROM A SINGLE IMAGE

作     者：Wei Wang Michael K.Ng 

作者机构：Department of MathematicsTongji UniversityShanghai 200092China Centre for Mathematical Imaging and Vision and Department of MathematicsHong Kong Baptist UniversityKowloon TongHong KongChina 

出 版 物：《Journal of Computational Mathematics》 (计算数学（英文）)

年 卷 期：2012年第30卷第1期

页      面：80-100页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0835[工学-软件工程] 0802[工学-机械工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 080201[工学-机械制造及其自动化] 

主　　题：Blind deconvolution Iterative methods Total variation Framelet Generalizedcross validation. 

摘      要：In this paper, we study two variational blind deblurring models for a single linage,The first model is to use the total variation prior in both image and blur, while the second model is to use the flame based prior in both image and blur. The main contribution of this paper is to show how to employ the generalized cross validation （GCV） method efficiently and automatically to estimate the two regularization parameters associated with the priors in these two blind motion deblurring models. Our experimental results show that the visual quality of restored images by the proposed method is very good, and they are competitive with the tested existing methods. We will also demonstrate the proposed method is also very efficient.