A fast algorithm for image super-resolution from blurred observations

Authors:
Nirmal K. Bose;Michael K. Ng;Andy C. Yau
Affiliations:
Spatial and Temporal Signal Processing Center, Department of Electrical Engineering, The Pennsylvania State University, University Park, PA;Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong;Department of Mathematics, Faculty of Science, The University of Hong Kong, Hong Kong, China
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

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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.