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Proceedings Paper

Interpolation by two-dimensional cubic convolution
Author(s): Jiazheng Shi; Stephen E. Reichenbach
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Paper Abstract

This paper presents results of image interpolation with an improved method for two-dimensional cubic convolution. Convolution with a piecewise cubic is one of the most popular methods for image reconstruction, but the traditional approach uses a separable two-dimensional convolution kernel that is based on a one-dimensional derivation. The traditional, separable method is sub-optimal for the usual case of non-separable images. The improved method in this paper implements the most general non-separable, two-dimensional, piecewise-cubic interpolator with constraints for symmetry, continuity, and smoothness. The improved method of two-dimensional cubic convolution has three parameters that can be tuned to yield maximal fidelity for specific scene ensembles characterized by autocorrelation or power-spectrum. This paper illustrates examples for several scene models (a circular disk of parametric size, a square pulse with parametric rotation, and a Markov random field with parametric spatial detail) and actual images -- presenting the optimal parameters and the resulting fidelity for each model. In these examples, improved two-dimensional cubic convolution is superior to several other popular small-kernel interpolation methods.

Paper Details

Date Published: 8 August 2003
PDF: 12 pages
Proc. SPIE 5108, Visual Information Processing XII, (8 August 2003); doi: 10.1117/12.497405
Show Author Affiliations
Jiazheng Shi, Univ. of Nebraska/Lincoln (United States)
Stephen E. Reichenbach, Univ. of Nebraska/Lincoln (United States)


Published in SPIE Proceedings Vol. 5108:
Visual Information Processing XII
Zeno J. Geradts; Zia-ur Rahman; Lenny I. Rudin; Robert A. Schowengerdt; Stephen E. Reichenbach, Editor(s)

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