Objectively Optimised Multisensor Image Fusion

A plethora of image fusion algorithms have been proposed recently, yet what are optimal fusion parameters that should be used for any multi-sensor dataset cannot be defined a priori. They could be learned by evaluating all available fusion strategies on large, representative datasets, but this is not practical and provides no guarantee that fusion performance will remain optimal should real input conditions differ from sample data. This paper proposes and examines the viability of a powerful framework for objectively optimal image fusion that explicitly optimises fusion performance for any set of input conditions. The idea is to integrate proven concepts used in objective image fusion evaluation metrics to optimally adapt the fusion process to the input conditions. Specific focus is on fusion for display, which has a broad appeal in a wide range of fusion applications as only metrics shown to be subjectively relevant are considered. The results show that the proposed framework achieves a considerable improvement in both the level and robustness of fusion performance for a wide array of multi-sensor images