Comparative Study of Image Fusion Methods

Abstract

Sensor networks are increasingly becoming an attractive method to collect information in a given area. However more than one sensors are required to providing the information, either because of their design or because of observational constraints. One possible solution to get all the required information about a particular scene or subject is data fusion. Multi-sensor data often presents complementary information about the region surveyed and data fusion provides an effective method to enable comparison, interpretation and analysis of such data. It is possible to have several images of the same scene providing different information about the same scene. This is because each image has been captured with a different sensor. In this paper we provide a method for evaluating the performance of image fusion algorithms. We define a set of measures of effectiveness for comparative performance analysis and then use them on the output of a number of fusion algorithms that have been applied to a set of real passive infrared (IR) and visible band imagery.