The benefits of image fusion for man-in-the-loop Detection, Recognition, and Identification (DRI) tasks are well known.
However, the performance of conventional image fusion systems is typically sub-optimal, as they fail to capitalise on
high-level information which can be abstracted from the imagery. As part of a larger study into an Intelligent Image
Fusion (I2F) framework, this paper presents a novel approach which exploits high-level cues to adaptively enhance the
fused image via feedback to the pixel-level processing. Two scenarios are chosen for illustrative application of the
approach, Situational Awareness and Anomalous Object Detection (AOD). In the Situational Awareness scenario,
motion and other cues are used to enhance areas of the image according to predefined tasks, such as the detection of
moving targets of a certain size. This yields a large increase in Local Signal-to-Clutter Ratio (LSCR) when compared to
a baseline, non-adaptive approach. In the AOD scenario, spatial and spectral information is used to direct a foveal-patch
image fusion algorithm. This demonstrates a significant increase in the Probability of Detection on test imagery whilst
simultaneously reducing the mean number of false alarms when compared to a baseline, non-foveal approach. This paper
presents the rationale for the I2F approach and details two specific examples of how it can be applied to address very
different applications. Design details and quantitative performance analysis results are reported.