Super-resolution image restoration from blurred observations

We study the problem of the reconstruction of a high-resolution image from several blurred low-resolution image frames. The image frames consist of blurred, decimated and noisy versions of a high-resolution image. The high-resolution image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that, with the periodic boundary condition, a high-resolution image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore high-resolution images in the aperiodic boundary condition.