Image fusion algorithm assessment using measures of complementary and redundant information

Abstract

Often various amounts of complementary information exist when imagery of the same scene is captured in different spectral bands. Image fusion should merge the available information within the source images into a single fused image that contains more relevant information compared to any single source image. The benefits of image fusion are more readily seen when the source images contain complementary information. Intuitively complementary information allows for measurable improvements in human task performance. However, quantifying the effect complementary information has on fusion algorithms remains open research. The goal of this study is to quantify the effect of complementary information on image fusion algorithm performance. Algorithm performance is assessed using a new performance metric, based on mutual information. Human perception experiments are conducted using controlled amounts of complementary information as input to a simple fusion process. This establishes the relationship between complementary information and task performance. The results of this study suggest a correlation exists between the proposed metric and identification task performance.