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Volume 30, Issue 1
On Algorithms for Automatic Deblurring from a Single Image

Wei Wang & Michael K. Ng

DOI: 10.4208/jcm.1110-m11si13

J. Comp. Math., 30 (2012), pp. 80-100.

Published online: 2012-02

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  • 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.

  • Keywords

Blind deconvolution, Iterative methods, Total variation, Framelet, Generalized cross validation.

  • AMS Subject Headings

65J22, 65K10, 68U10.

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{JCM-30-80, author = {Wei Wang and Michael K. Ng}, title = {On Algorithms for Automatic Deblurring from a Single Image}, journal = {Journal of Computational Mathematics}, year = {2012}, volume = {30}, number = {1}, pages = {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.

}, issn = {1991-7139}, doi = {https://doi.org/10.4208/jcm.1110-m11si13}, url = {http://global-sci.org/intro/article_detail/jcm/8418.html} }
TY - JOUR T1 - On Algorithms for Automatic Deblurring from a Single Image AU - Wei Wang & Michael K. Ng JO - Journal of Computational Mathematics VL - 1 SP - 80 EP - 100 PY - 2012 DA - 2012/02 SN - 30 DO - http://doi.org/10.4208/jcm.1110-m11si13 UR - https://global-sci.org/intro/article_detail/jcm/8418.html KW - Blind deconvolution, Iterative methods, Total variation, Framelet, Generalized cross validation. AB -

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.

Wei Wang and Michael K. Ng. (2012). On Algorithms for Automatic Deblurring from a Single Image. Journal of Computational Mathematics. 30 (1). 80-100. doi:10.4208/jcm.1110-m11si13
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