1 research outputs found
An Objective Evaluation Metric for image fusion based on Del Operator
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 ({\mu},{\sigma}) 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.Comment: 22 pages, 14 Figure