Super-Resolution Image Reconstruction using the Discontinuity Adaptive ICM

We propose a Bayesian approach for the super resolution image reconstruction (SRIR) problem using a Markov random field (MRF) for image characterization. SRIR consists in using a set of low-resolution (LR) images from the same scene to generate a high-resolution (HR) estimate of the original object. Using a Bayesian formulation, it is possible to incorporate previously known spatial information about the HR image to be estimated. In our approach, the iterated conditional modes (ICM) algorithm is used to find the maximum a posteriori (MAP) solution, and a discontinuity adaptive framework is used to overcome the oversmoothness inherent to MAP-MRF formulations. To evaluate the capability of the algorithm in reconstructing the actual image, we used the universal image quality index (UIQI). According to this index, the proposed method produced accurate results.