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Proceedings Paper

Two-Parameter Cubic Convolution For Image Reconstruction
Author(s): Stephen E. Reichenbach; Stephen K. Park
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Paper 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.

Paper Details

Date Published: 1 November 1989
PDF: 8 pages
Proc. SPIE 1199, Visual Communications and Image Processing IV, (1 November 1989); doi: 10.1117/12.970093
Show Author Affiliations
Stephen E. Reichenbach, University of Nebraska (United States)
Stephen K. Park, College of William and Mary (United States)

Published in SPIE Proceedings Vol. 1199:
Visual Communications and Image Processing IV
William A. Pearlman, Editor(s)

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