Mutual information improves image fusion quality assessments

The goal of image fusion techniques is to combine and preserve all of the important visual information present in multiple input images in a single output image. In many applications, the quality of the fused images is of fundamental importance and is usually assessed by visual analysis subjective to the interpreter. Many objective quality metrics exist in image fusion,1 but when no clearly-defined ground truth exists, we must construct an ideal fused image to use as a reference for comparing with the experimental results. Among the available ways to measure quality, both the mean square error (MSE) and signal-to-noise ratio (SNR) metrics are widely employed because they are easy to calculate and typically have low computational costs. Other metrics such as the Wigner signal-to-noise ratio (SNRw) and structural similarity quality index (SSIM)3 have been recently proposed, but these metrics require a reference image together with the processed image. Non-reference metrics are much more difficult to define, as knowledge of ground truth is not assumed. These metrics are not relative to an original image.4 Here we use mutual information (MI) as an information measure for evaluating image fusion performance. This measure represents how much of the information in the final fused image was obtained from the input images.