Determining the parameters in regularized super-resolution reconstruction

Abstract

We derive a novel method to determine the parameters for regularized super-resolution problems. The proposed approach relies on the Joint Maximum a Posteriori (JMAP) estimation technique. The classical JMAP technique provides solutions at low computational cost, but it may be unstable and presents multiple local minima. We propose to stabilize the JMAP estimation, while achieving a cost function with an unique global solution, by assuming a gamma prior distribution for the hyperparameters. The resulting fidelity is similar to the quality provided by the best methods such as the Evidence, which are computationally expensive. Experimental results illustrate the low complexity and stability of the proposed method.