Abstract
Entropy-based measures are popular for objective image fusion quality assessment due to a small parameter set for implementation and independency of ground-truth image as the reference for evaluation. We focus on Tsallis entropy and consider mutual entropy and entropic distance as the two entropic measures for image fusion quality assessment. To perform an in-depth analysis over quality measures and evaluate to what extent they are able to fulfill desired behaviors that are expected from ideal image fusion quality measures, we separately conduct theoretical analysis for each of them. To this goal, we employ an image formation model to obtain a closed-form expression for quality while weighted averaging is used as fusion algorithm. Our study shows that the so-called measures do not always satisfy the expected desired behaviors. We also provide explanations for unexpected behaviors that can improve the accuracy of image fusion quality measure in application. Investigations on real images are also performed, and the results verify the output of theoretical analysis.
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