System fusion in passive sensing using a modified hopfield network

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 propose to exploit the idea of neural-network-based imaging system fusion. The fusion problem is stated and treated as an aggregate inverse problem of restoration 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 the energy function of the multistate Hopfield-type neural network, which integrates the model parameters of all sensor systems incorporating a priori and measurement information. Simulation examples are presented to illustrate the good overall performance of the fused restoration achieved with the proposed neural network algorithm.