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. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
CITATION STYLE
Bose, N. K., Ng, M. K., & Yau, A. C. (2006). A fast algorithm for image super-resolution from blurred observations. Eurasip Journal on Applied Signal Processing, 2006, 1–14. https://doi.org/10.1155/ASP/2006/35726
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