By Topic

Superresolution algorithms for a modified Hopfield neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Abbiss, J.B. ; Spectron Dev. Lab. Inc., Costa Mesa, CA, USA ; Brames, B.J. ; Fiddy, M.A.

The authors describe the implementation of a superresolution (or spectral extrapolation) procedure on a neural network, based on the Hopfield (1982) model. They show the computational advantages and disadvantages of such an approach for different coding schemes and for networks consisting of very simple two-state elements as well as those made up of more complex nodes capable of representing a continuum. It is demonstrated that, with the appropriate hardware, there is a computational advantage in using the Hopfield architecture over some alternative methods for computing the same solution. The relationship between a particular mode of operation of the neural network and the regularized Gerchberg (1974) and Papoulis (1975) algorithm is also discussed

Published in:

Signal Processing, IEEE Transactions on  (Volume:39 ,  Issue: 7 )