Image fusion experiment for information content

Recent developments in image fusion have produced a variety of approaches like image overlay, image sharpening, and image cueing through pixel, feature, or region/shape combinations. The applicability of these approaches and techniques differ on the image content, contextual information, and generalized metrics of image fusion gain. An image fusion gain can be assessed relative to information gain or entropy reduction. In this paper, we are interested in exploring the techniques and data available with the image fusion toolbox (from www.imagefusion.org) to assess the use of relative entropy analysis for metric evaluation, image fusion gain calculations, and assessment of fused images as templates for automatic target recognition. Examples are demonstrated for medical (PET/MRI), (CT/MRI) and environment (visible/infrared) examples. A mutual information measure of the image fusion quality can be an effective tool to characterize image terrain, content, and contextual information to cue higher-level fusion algorithms.