An Objective Evaluation Metric for image fusion based on Del Operator

TL;DR

This paper introduces a new parameter-free, illumination-independent objective metric for image fusion quality assessment based on the Del operator, which correlates well with human perception.

Contribution

The paper presents a novel, easy-to-implement evaluation metric for image fusion that outperforms existing metrics in aligning with human visual perception.

Findings

The proposed metric is parameter-free and illumination-independent.

It shows better agreement with human perception than existing metrics.

It effectively evaluates diverse multimodal medical image fusion methods.

Abstract

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.