Abstract
Fusion of infrared and visible image is a technology which combines information from two different sensors for the same scene. It also gives extremely effective information complementation, which is widely used for the monitoring systems and military fields. Due to limited field depth in an imaging device, visible images can't identify some targets that may not be apparent due to poor lighting conditions or because that the background color is similar to the target. To deal with this problem, a simple and efficient image fusion approach of infrared and visible images is proposed to extract target's details from infrared images and enhance the vision in order to improve the performance of monitoring systems. This method depends on maximum and minimum operations in neutrosophic fuzzy sets. Firstly, the image is transformed from its spatial domain to the neutrosophic domain which is described by three membership sets: truth membership, indeterminacy membership, and falsity membership. The indeterminacy in the input data is handled to provide a comprehensive fusion result. Finally, deneutrosophicised process is made which means that the membership values are retransformed into a normal image space. At the end of the study, experimental results are applied to evaluate the performance of this approach and compare it to the recent image fusion methods using several objective evaluation criteria. These experiments demonstrate that the proposed method achieves outstanding visual performance and excellent objective indicators.