Maximum a posteriori super resolution based on simultaneous non-stationary restoration, interpolation and fast registration

In this paper we propose a maximum a posteriori (MAP) framework for the super resolution problem, i.e. reconstructing high-resolution images from shifted, low-resolution degraded observations. In this framework the restoration, interpolation and registration subtasks of this problem are preformed simultaneously. The main novelties of this work are the use of a new hierarchical non-stationary edge adaptive prior for the super resolution problem, and an efficient implementation of this methodology in the discrete Fourier transform (DFT) domain. We present examples with real data that demonstrate the advantages of this methodology.