Analysis and implementation of the Lagrange programming neural network for image restoration

Bin Wang; Thomas F. Krile

doi:10.1117/12.205181

6 April 1995 Analysis and implementation of the Lagrange programming neural network for image restoration

Bin Wang, Thomas F. Krile

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205181
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

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Bin Wang and Thomas F. Krile "Analysis and implementation of the Lagrange programming neural network for image restoration", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205181

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KEYWORDS

Neurons

Image restoration

Neural networks

Computer programming

Signal to noise ratio

Image processing

Interference (communication)

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