On Algorithms for Automatic Deblurring from a Single Image
Wei Wang 1, Michael K. Ng 21 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
Received 2011-2-28 Accepted 2011-08-15
Available online 2012-1-09
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.
Key words: Blind deconvolution, Iterative methods, Total variation, Framelet, Generalized cross validation.
AMS subject classifications: 65J22, 65K10, 68U10.
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