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A cellular non‐linear network for image fusion based on data regularization
Author(s) -
Anzalone Andrea,
Bizzarri Federico,
Storace Marco,
Parodi Mauro
Publication year - 2006
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.354
Subject(s) - regularization (linguistics) , computer science , fusion , cellular neural network , image (mathematics) , algorithm , artificial intelligence , mathematics , mathematical optimization , artificial neural network , philosophy , linguistics
In this paper, a synthesis method developed in the last few years is applied to derive a cellular non‐linear network (CNN) able to find an approximate solution to a variational image‐fusion problem. The functional to be minimized is based on regularization theory and takes into account two complementary principles, namely, knowledge source corroboration and belief enhancement/withdrawal, both typical of data‐fusion approaches. The obtained CNN has been tested by simulations (i.e. by numerically integrating the circuit state equations) in some case studies. The quality of the results is good, as turns out from comparisons with some standard methods. Copyright © 2006 John Wiley & Sons, Ltd.