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A Fast Algorithm for Image Super-Resolution from Blurred Observations


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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Correspondence to Nirmal K Bose.

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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).

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  • Fourier Transform
  • Computer Simulation
  • Fast Fourier Transform
  • Periodic Boundary
  • Quantum Information