Two-parameter cubic convolution for image reconstruction

Stephen E. Reichenbach, Stephen K. Park

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

This paper presents an analysis of a recently-proposed two-parameter piecewise-cubic convolution algorithm for image reconstruction. The traditional cubic convolution algorithm is a one-parameter, interpolating function. With the second parameter, the algorithm can also be approximating. The analysis leads to a Taylor series expansion for the average square error due to sampling and reconstruction as a function of the two parameters. This analysis indicates that the additional parameter does not improve the reconstruction fidelity-the optimal two-parameter convolution kernel is identical to the optimal kernel for the traditional one-parameter algorithm. Two methods for constructing the optimal cubic kernel are also reviewed.

Original languageEnglish (US)
Pages (from-to)833-840
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1199
DOIs
StatePublished - Nov 1 1989

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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