Validation and Correlation Analysis of Metrics for Evaluating Performance of Image Fusion

Image fusion performance evaluation aims at providing an efficient and accurate method for the fusion model choosing, parameter optimizing and the like. By analyzing the mechanism of existing metrics in theory and testing the performance of metrics and correlations with each other experimentally, the paper presents an effective metric set selection strategy. First of all, existing metrics are classified into three categories: statistics-based, information-based and human-visual-system based classes; secondly, we enumerate the classical or the latest metrics for each class. In addition,we test the performance of objective evaluating metrics in terms of correct ranking by running on a standard data set,and the results indicate that human-visual-system based metrics are superior to others. Finally, we explore correlations among metrics using Spearman correlation coefficient. Experimental results indicate that we can choose a proper objective evaluating metric set by means of performances and correlations of metrics.