Feature-Based Image Fusion Quality Metrics

Authors:
Mohammed Hossny;Saeid Nahavandi;Doug Crieghton
Affiliations:
Intelligent Systems Research Lab, Deakin University, Australia;Intelligent Systems Research Lab, Deakin University, Australia;Intelligent Systems Research Lab, Deakin University, Australia
Venue:
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Year:
2008

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Abstract

Image fusion quality metrics have evolved from image processing quality metrics. They measure the quality of fused images by estimating how much localized information has been transferred from the source images into the fused image. However, this technique assumes that it is actually possible to fuse two images into one without any loss. In practice, some features must be sacrificed and relaxed in both source images. Relaxed features might be very important, like edges, gradients and texture elements. The importance of a certain feature is application dependant. This paper presents a new method for image fusion quality assessment. It depends on estimating how much valuable information has not been transferred.