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ON ALGORITHMS FOR AUTOMATIC DEBLURRING FROM A SINGLE IMAGE 被引量:1

ON ALGORITHMS FOR AUTOMATIC DEBLURRING FROM A SINGLE IMAGE
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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.
出处 《Journal of Computational Mathematics》 SCIE CSCD 2012年第1期80-100,共21页 计算数学(英文)
关键词 Blind deconvolution Iterative methods Total variation Framelet Generalizedcross validation. Blind deconvolution, Iterative methods, Total variation, Framelet, Generalizedcross validation.
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