Open Access

A Fast Algorithm for Image Super-Resolution from Blurred Observations

EURASIP Journal on Advances in Signal Processing20062006:035726

https://doi.org/10.1155/ASP/2006/35726

Received: 1 December 2004

Accepted: 7 April 2005

Published: 22 February 2006

Abstract

We study the problem of 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. Computer simulations are given to illustrate the effectiveness of the proposed approach.

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Authors’ Affiliations

(1)
Spatial and Temporal Signal Processing Center, Department of Electrical Engineering, The Pennsylvania State University
(2)
Department of Mathematics, Hong Kong Baptist University
(3)
Department of Mathematics, Faculty of Science, The University of Hong Kong

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Copyright

© Bose et al. 2006