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Image restoration using a modified Hopfield network.

Authors
  • Paik, J K
  • Katsaggelos, A K
Type
Published Article
Journal
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Publication Date
Jan 01, 1992
Volume
1
Issue
1
Pages
49–63
Identifiers
PMID: 18296139
Source
Medline
License
Unknown

Abstract

A modified Hopfield neural network model for regularized image restoration is presented. The proposed network allows negative autoconnections for each neuron. A set of algorithms using the proposed neural network model is presented, with various updating modes: sequential updates; n-simultaneous updates; and partially asynchronous updates. The sequential algorithm is shown to converge to a local minimum of the energy function after a finite number of iterations. Since an algorithm which updates all n neurons simultaneously is not guaranteed to converge, a modified algorithm is presented, which is called a greedy algorithm. Although the greedy algorithm is not guaranteed to converge to a local minimum, the l (1) norm of the residual at a fixed point is bounded. A partially asynchronous algorithm is presented, which allows a neuron to have a bounded time delay to communicate with other neurons. Such an algorithm can eliminate the synchronization overhead of synchronous algorithms.

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