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Image restoration using a modified Hopfield network

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2 Author(s)
J. K. Paik ; Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA ; A. K. Katsaggelos

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

Published in:

IEEE Transactions on Image Processing  (Volume:1 ,  Issue: 1 )