We address a new approach to the problem of improving the quality of remote
-sensing images obtained with several passive systems, in which case we pro
pose to exploit the idea of neural-network-based imaging system fusion. The
fusion problem is stated and treated as an aggregate inverse problem of re
storation of the original image from the degraded data provided by several
image-formation systems. The non-parametric maximum entropy regularization
methodology is applied to solve the restoration problem with the control of
balance between the gained spatial resolution and noise suppression in the
resulting image. The restoration and fusion are performed by minimizing th
e energy function of the multistate Hopfield-type neural network, which int
egrates the model parameters of all sensor systems incorporating a priori a
nd measurement information. Simulation examples are presented to illustrate
the good overall performance of the fused restoration achieved with the pr
oposed neural network algorithm. (C) 2001 The Franklin Institute. Published
by Elsevier Science Ltd. All rights reserved.