Two-Parameter Cubic Convolution For Image Reconstruction

Stephen E. Reichenbach; Stephen K. Park

doi:10.1117/12.970093

1 November 1989 Two-Parameter Cubic Convolution For Image Reconstruction

Stephen E. Reichenbach, Stephen K. Park

Proceedings Volume 1199, Visual Communications and Image Processing IV; (1989) https://doi.org/10.1117/12.970093
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

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.

Citation Download Citation

Stephen E. Reichenbach and Stephen K. Park "Two-Parameter Cubic Convolution For Image Reconstruction", Proc. SPIE 1199, Visual Communications and Image Processing IV, (1 November 1989); https://doi.org/10.1117/12.970093

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