IEEE Trans Image Process - Blind deconvolution using generalized cross-validation approach to regularization parameter estimation.

Tópicos

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{ method(2212) result(1239) propos(1039) }
{ general(901) number(790) one(736) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(2830) propos(1344) filter(1198) }
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Resumo

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.

Resumo Limpo

paper propos present algorithm total variat tvbase blind deconvolut unknown imag blur can estim within altern minim framework general crossvalid gcv method regular paramet associ unknown imag blur can updat altern minim step experiment result confirm perform propos algorithm better variat bayesian blind deconvolut algorithm studentst prior total variat prior

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