Year: 2012
Journal of Computational Mathematics, Vol. 30 (2012), Iss. 1 : pp. 80–100
Abstract
In this paper, we study two variational blind deblurring models for a single image. The first model is to use the total variation prior in both image and blur, while the second model is to use the frame 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.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1110-m11si13
Journal of Computational Mathematics, Vol. 30 (2012), Iss. 1 : pp. 80–100
Published online: 2012-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 21
Keywords: Blind deconvolution Iterative methods Total variation Framelet Generalized cross validation.