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In this paper, we propose a maximum a posteriori framework for the super-resolution problem, i.e., reconstructing high-resolution images from shifted, rotated, low-resolution de-graded observations. The main contributions of this work are two; first, the use of a new locally adaptive edge preserving prior for the super-resolution problem. Second an efficient two-step reconstruction methodology that includes first an initial registration using only the low-resolution degraded observations. This is followed by a fast iterative algorithm implemented in the discrete Fourier transform domain in which the restoration, interpolation and the registration subtasks of this problem are preformed simultaneously. We present examples with both synthetic and real data that demonstrate the advantages of the proposed framework. [ABSTRACT FROM AUTHOR]

Using a stochastic framework, we propose two algo- rithms for the problem of obtaining a single high-resolution image from multiple noisy, blurred, and undersampled images. The first is based on a Bayesian formulation that is implemented via the ex- pectation maximization algorithm. The second is based on a max- imum a posteriori formulation. In both of oUr formulations, the registration, noise, and image statistics tire treated as unknown parameters. These unknown parameters and the high-resolution image are estimated jointly based on the available observations. We present an efficient implementation of these algorithms in the fre- quency domain that allows their application to large images. Sim- ulations are presented that test and compare the proposed algo- rithms. [ABSTRACT FROM AUTHOR]