Abstract
In this paper, we propose and present an algorithm for total variation (TV)-based blind deconvolution. Both the unknown image and blur can be estimated within an alternating minimization framework. With the generalized cross-validation (GCV) method, the regularization parameters associated with the unknown image and blur can be updated in alternating minimization steps. Experimental results confirm that the performance of the proposed algorithm is better than variational Bayesian blind deconvolution algorithms with Student's-t priors or a total variation prior.
Original language | English |
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Article number | 5565466 |
Pages (from-to) | 670-680 |
Number of pages | 11 |
Journal | IEEE Transactions on Image Processing |
Volume | 20 |
Issue number | 3 |
DOIs | |
Publication status | Published - Mar 2011 |
Scopus Subject Areas
- Software
- Computer Graphics and Computer-Aided Design
User-Defined Keywords
- Alternating minimization
- blind deconvolution
- generalized cross validation (GCV)
- regularization parameters
- total variation (TV)