A data-consistent linear prediction method for image reconstruction from finite fourier samples

Christopher P. Hess, Zhi Pei Liang

Research output: Contribution to journalArticlepeer-review

Abstract

Linear prediction (LP) methods have been widely used for high-resolution spectral estimation from finite Fourier samples. Their application to image reconstruction, on the other hand, has been markedly less successful. In this article, we present an improved LP method for high-resolution image reconstruction. The distinguishing feature of the proposed method is its use of a generalized series model to enforce the data consistency constraint to compensate for reconstruction error resulting from LP modeling. Several reconstruction examples from magnetic resonance imaging data are included to demonstrate the performance of the method.

Original languageEnglish (US)
Pages (from-to)136-140
Number of pages5
JournalInternational Journal of Imaging Systems and Technology
Volume7
Issue number2
DOIs
StatePublished - Jan 1 1996

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Software
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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