An Objective Evaluation Metric for image fusion based on Del Operator
This work addresses the need for a reliable, parameter-free evaluation metric in image fusion, particularly for medical imaging, though it is incremental as it builds on existing non-reference metrics.
The authors tackled the problem of evaluating image fusion quality by proposing a novel objective metric based on the Del operator, which outperformed thirteen existing metrics in aligning with human perception and assessing fusion methods across multimodal medical images.
In this paper, a novel objective evaluation metric for image fusion is presented. Remarkable and attractive points of the proposed metric are that it has no parameter, the result is probability in the range of [0, 1] and it is free from illumination dependence. This metric is easy to implement and the result is computed in four steps: (1) Smoothing the images using Gaussian filter. (2) Transforming images to a vector field using Del operator. (3) Computing the normal distribution function (μ,σ) for each corresponding pixel, and converting to the standard normal distribution function. (4) Computing the probability of being well-behaved fusion method as the result. To judge the quality of the proposed metric, it is compared to thirteen well-known non-reference objective evaluation metrics, where eight fusion methods are employed on seven experiments of multimodal medical images. The experimental results and statistical comparisons show that in contrast to the previously objective evaluation metrics the proposed one performs better in terms of both agreeing with human visual perception and evaluating fusion methods that are not performed at the same level.