Estimation of the parameters in regularized simultaneous super-resolution
We describe a method for automatic determination of the regularization parameters for the class of simultaneous super-resolution (SR) algorithms. This method, proposed in (Zibetti et al., 2008c), is based on the joint maximum a posteriori (JMAP) estimation technique, which is a fast alternative to estimate the parameters. However, the classical JMAP technique can be unstable and may generate 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. In this work, experimental results are provided to illustrate the effectiveness of the proposed method for automatic determination of the regularization parameters for the simultaneous SR. Moreover, we contrast the proposed method to a reference method with known fixed parameters as well as to other parameter selection methods based on the L-curve. These results validate the proposed method as a very attractive alternative for estimating the regularization parameters.
regularization simultaneous super-resolution jmap bayesian estimation