Abstract
This paper proposes a Bayesian super-resolution method for reconstructing a high resolution image from multiple lower resolution ones. The main contribution is to introduce an automatic method for determining regularization parameters for balancing a prior distribution and a likelihood function in the Bayesian model. The regularization parameters are determined by minimizing the Akaike Bayesian information criterion (ABIC). In order to calculate the ABIC, a second-order approximation is used to evaluate the multiple integral of the posterior distribution, because it is difficult to analytically evaluate the integral. Experimental results show the effectiveness of the proposed super-resolution method.
