Abstract
In this paper, we present a novel metric for evaluation of image fusion algorithms, based on evaluation of similarity of regions in images to be fused with the corresponding regions in the fused image. The metric uses several factors to quantify the importance of regions in each of the input images, such as contrast, size, and shape of region. The similarity of the corresponding regions in an input image and the fused image is measured using a wavelet-based mutual information measure. Experimental results show that the proposed metric's ranking of different image fusion methods is more consistent with the subjective quality of the fused image than the state-of-the-art image fusion metrics.
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