ON ALGORITHMS FOR AUTOMATIC DEBLURRING FROM A SINGLE IMAGE  

作　　者：Wei Wang Michael K.Ng 

机构地区：[1]Department of Mathematics,Tongji University,Shanghai 200092,China [2]Centre for Mathematical Imaging and Vision and Department of Mathematics,Hong Kong Baptist University,Kowloon Tong,Hong Kong,China

出　　处：《Journal of Computational Mathematics》2012年第1期80-100,共21页计算数学（英文）

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.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.

关 键 词：Blind deconvolution Iterative methods Total variation Framelet Generalizedcross validation. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP273.4[自动化与计算机技术—控制科学与工程]