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Proceedings Paper

Analysis and implementation of the Lagrange programming neural network for image restoration
Author(s): Bin Wang; Thomas F. Krile
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Paper Abstract

In this paper, we propose a modification to the Lagrange programming neural network (LPNN) and its implementation procedure for maximum entropy image restoration with signal independent noise. Our approach has better transient behavior and convergence speed without imposing strict restrictions on the initial information and starting point. Gray level real images with practical size (256 X 256) can be restored in less than 10 iterations, which greatly reduces computational complexity and brings the maximum entropy image restoration technique to a practical stage. Computer simulation results and detailed discussions are provided, with comparisons to restoration using a Hopfield neural network.

Paper Details

Date Published: 6 April 1995
PDF: 13 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205181
Show Author Affiliations
Bin Wang, Texas Tech Univ. (United States)
Thomas F. Krile, Texas Tech Univ. (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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