Two-parameter cubic convolution for image reconstruction

Stephen E. Reichenbach, Stephen K. Park

Research output: Contribution to journalArticle

8 Citations (Scopus)

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

Fingerprint

Image Reconstruction
image reconstruction
Image reconstruction
Convolution
convolution integrals
Two Parameters
Optimal Kernel
kernel
Taylor Series Expansion
Taylor series
Optimal Parameter
Fidelity
Sampling
series expansion
sampling

ASJC Scopus subject areas

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

Cite this

Two-parameter cubic convolution for image reconstruction. / Reichenbach, Stephen E.; Park, Stephen K.

In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 1199, 01.11.1989, p. 833-840.

Research output: Contribution to journalArticle

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