A Fast Algorithm for Image Super-Resolution from Blurred Observations

  • Nirmal K BoseEmail author
  • Michael K Ng
  • Andy C Yau
Open Access
Research Article
Part of the following topical collections:
  1. Super-Resolution Imaging: Analysis, Algorithms, and Applications


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.


Fourier Transform Computer Simulation Fast Fourier Transform Periodic Boundary Quantum Information 


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Copyright information

© Bose et al. 2006

Authors and Affiliations

  1. 1.Spatial and Temporal Signal Processing Center, Department of Electrical EngineeringThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of MathematicsHong Kong Baptist UniversityKowloon TongHong Kong
  3. 3.Department of MathematicsFaculty of Science, The University of Hong KongHong KongChina

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