Registering two images produced by two separate imaging sensors having different detector sizes and fields of view requires one of the images to undergo transformation operations that may cause its overall quality to degrade with regards to visual task performance. This possible change in image quality could add to an already existing difference in measured task performance. Ideally, a fusion algorithm would take as input unaltered outputs from each respective sensor used in the process. Therefore, quantifying how well an image fusion algorithm performs should be base lined to whether the fusion algorithm retained the performance benefit achievable by each independent spectral band being fused. This study investigates an identification perception experiment using a simple and intuitive process for discriminating between image fusion algorithm performances. The results from a classification experiment using information theory based image metrics is presented and compared to perception test results. The results show an effective performance benchmark for image fusion algorithms can be established using human perception test data. Additionally, image metrics have been identified that either agree with or surpass the performance benchmark established.

Date Published: 18 May 2012
PDF: 13 pages
Proc. SPIE 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII, 835513 (18 May 2012); doi: 10.1117/12.918418

Published in SPIE Proceedings Vol. 8355:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII
Gerald C. Holst; Keith A. Krapels, Editor(s)