Department of Mathematics
On algorithms for automatic deblurring from a single image
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. Copyright 2012 by AMSS, Chinese Academy of Sciences.
Blind deconvolution, Framelet, Generalized cross validation, Iterative methods, Total variation
Source Publication Title
Journal of Computational Mathematics -International Edition-
Link to Publisher's Edition
Wang, W., & Ng, M. (2012). On algorithms for automatic deblurring from a single image. Journal of Computational Mathematics -International Edition-, 30 (1), 80-100. https://doi.org/10.4208/jcm.1110-m11si13