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A Fast Algorithm for Image Super-Resolution from Blurred Observations

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 035726 (2006) Cite this article

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 and Affiliations

  1. Spatial and Temporal Signal Processing Center, Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA

    Nirmal K Bose & Andy C Yau

  2. Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Michael K Ng

  3. Department of Mathematics, Faculty of Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China

    Andy C Yau

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  1. Nirmal K Bose
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  2. Michael K Ng
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  3. Andy C Yau
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Correspondence to Nirmal K Bose.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Bose, N.K., Ng, M.K. & Yau, A.C. A Fast Algorithm for Image Super-Resolution from Blurred Observations. EURASIP J. Adv. Signal Process. 2006, 035726 (2006). https://doi.org/10.1155/ASP/2006/35726

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

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