Information Representation for Image Fusion Evaluation

The considerable number of image fusion algorithms available today vary widely in terms of fusion performance and robust fusion assessment tools have become a target of considerable research. Based on a variety of localised or global evaluations of image statistics and structure between the inputs and the fused image, available objective fusion evaluation metrics use a number of different information representation and information loss models. This paper explores the definition of an optimal information representation for the evaluation of multisensor image fusion and how it can be defined based on the actual application of fused information. Extensive evaluations of a considerable data set of subjectively annotated fused images with a variety of information representation approaches implemented using three different global fusion evaluation frameworks are presented. The results show that if used with correct information representation models global, statistical approaches can yield significantly better fusion evaluation performance than existing methods