Edge Information Based Image Fusion Metrics Using Fractional Order Differentiation and Sigmoidal Functions

In recent years, the number of image fusion schemes presented by the research community has increased significantly. Measuring the performance of these schemes is an important issue. In this work, we introduce three quantitative fusion metrics to assess the quality of an image fusion algorithm. The proposed metrics rely on edge information that is obtained using fractional order differentiation. Edge and orientation strengths are fed into three sigmoidal functions separately for estimating the values of three normalized weighted metrics for the fused image corresponding to source images. The experiments on the multi-focus, infrared-visible and medical image fusion pairs demonstrate that the proposed fusion metrics are perceptually meaningful and outperform some of the state-of-the-art metrics. This work was supported in part by the project (Prediction of diseases through computer assisted diagnosis system using images captured by minimally-invasive and non-invasive modalities), Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India, under Grant SPARC-MHRD-231, in part by the project of Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic, under Grant UHK-FIMGE-2020, and in part by the IT4Neuro—project of the Ministry of Education, Youth and Sports of Czech Republic under Project ERDF CZ.02.1.01/0.0/0.0/18 _069/0010054.