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Total variation super resolution using a variational approach

8 years 9 months ago
Total variation super resolution using a variational approach
In this paper we propose a novel algorithm for super resolution based on total variation prior and variational distribution approximations. We formulate the problem using a hierarchical Bayesian model where the reconstructed high resolution image and the model parameters are estimated simultaneously from the low resolution observations. The algorithm resulting from this formulation utilizes variational inference and provides approximations to the posterior distributions of the latent variables. Due to the simultaneous parameter estimation, the algorithm is fully automated so parameter tuning is not required. Experimental results show that the proposed approach outperforms some of the state-of-the-art super resolution algorithms.
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICIP
Authors S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos
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