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Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation
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DOI：10.1109/TIP.2010.2073474.png)
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Abstract
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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.
Keywords:
Alternating minimization
blind deconvolution
generalized cross validation (GCV)
regularization parameters
total variation (TV)
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