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Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation

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2 Author(s)
Haiyong Liao ; Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong ; Michael K. Ng

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.

Published in:

IEEE Transactions on Image Processing  (Volume:20 ,  Issue: 3 )