%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi@80/2008/07.17.15.53
%2 sid.inpe.br/sibgrapi@80/2008/07.17.15.53.20
%@doi 10.1109/SIBGRAPI.2008.21
%T Regularized simultaneous super-resolution with automatic determination of the parameters
%D 2008
%A Zibetti, Marcelo Victor Wust,
%A Mayer, Joceli,
%A Bazan, Fermín Sinforiano Viloche,
%@affiliation Federal University of Santa Catarina
%@affiliation Federal University of Santa Catarina
%@affiliation Federal University of Santa Catarina
%E Jung, Cláudio Rosito,
%E Walter, Marcelo,
%B Brazilian Symposium on Computer Graphics and Image Processing, 21 (SIBGRAPI)
%C Campo Grande, MS, Brazil
%8 12-15 Oct. 2008
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K simultaneous super-resolution, regularization, Bayesian estimation, JMAP.
%X We derive a novel method for automatic determination of the regularization parameters applicable for the class of simultaneous super-resolution (SR) algorithms. The proposed method is based on the classical joint maximum a posteriori (JMAP) estimation technique, which is a fast alternative to estimate the parameters. Unfortunately, the classical JMAP technique can be unstable and generates multiple local minima. In order to stabilize the JMAP estimation, while achieving a cost function with a unique global solution, we derive an improved solution by modeling the JMAP hyperparameters with a gamma prior distribution. Experimental results illustrate the effectiveness of the proposed method for automatic determination of the regularization parameters for the simultaneous SR. We also contrast the proposed method to a reference method named KNOWN. KNOWN is a MAP based simultaneous SR algorithm where the parameters are fixed, either known a priori or extracted from the high-resolution frames which are not usually available in practice.
%@language en
%3 zibetti-SimultaneousParameter.pdf