An efficient algorithm for superresolution in medium field imaging

Andy C. Yau*, N. K. Bose, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

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
Volume18
Issue number2-3
DOIs
Publication statusPublished - Sep 2007

Scopus Subject Areas

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

User-Defined Keywords

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

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