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Proceedings Paper

Adaptive generalized stack filtering under the mean-absolute-error criterion
Author(s): Lin Yin; Jaakko T. Astola; Yrjo A. Neuvo
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Paper Abstract

A new adaptive algorithm is developed in this paper for determining optimal generalized stack (GS) filters under the mean absolute error criterion. This algorithm, based on the neural network representation of Boolean functions, is much more efficient than the traditional truth table based algorithms. This is because: (1) the number of variables to represent a GS filter is considerably reduced when a set of neurons is used to represent a GS filter, where the number of the variables is proportional to the filter window width, and (2) the procedure of enforcing the stacking constraints of GS filters is greatly simplified since a sufficient condition is derived under which the neurons satisfy the stacking property. Experimental results from image restoration are provided to demonstrate the performance of the new adaptive GS filters.

Paper Details

Date Published: 1 April 1992
PDF: 11 pages
Proc. SPIE 1658, Nonlinear Image Processing III, (1 April 1992); doi: 10.1117/12.58362
Show Author Affiliations
Lin Yin, Tampere Univ. of Technology (Finland)
Jaakko T. Astola, Tampere Univ. of Technology (Finland)
Yrjo A. Neuvo, Tampere Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 1658:
Nonlinear Image Processing III
Edward R. Dougherty; Jaakko T. Astola; Charles G. Boncelet Jr., Editor(s)

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