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.
A Cellular Nonlinear Network for image fusion based on data regularization
STORACE, MARCO;PARODI, MAURO
2006-01-01
Abstract
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.
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