An efficient algorithm for superresolution in medium field imaging

Andy C. Yau*, N. K. Bose, Michael K. Ng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

4 Citations (Scopus)
10 Downloads (Pure)


In this paper, we study the problem of reconstruction of a high-resolution (HR) image from several blurred low-resolution (LR) image frames in medium field. The image frames consist of blurred, decimated, and noisy versions of a HR image. The HR 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 HR image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore HR images in the aperiodic boundary condition. Computer simulations are given to illustrate the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)173-188
Number of pages16
JournalMultidimensional Systems and Signal Processing
Issue number2-3
Publication statusPublished - Sept 2007

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • Superresolution
  • Medium field
  • Preconditioned conjugate gradient method
  • Fast Fourier transforms
  • Toeplitz matrix
  • Deblussing


Dive into the research topics of 'An efficient algorithm for superresolution in medium field imaging'. Together they form a unique fingerprint.

Cite this