An infrared and visible image fusion using knowledge measures for intuitionistic fuzzy sets and Swin Transformer

The objectives of infrared and visible image fusion are to generate a single image that includes significant objects and rich texture information. However, the current deep-learning methods ignore uncertainty in the decision-making process during the fusion phase. To address this issue, we propose a novel infrared and visible image fusion method using a Swin Transformer and knowledge measures of intuitionistic fuzzy sets (IFSs) named SWKIF-Fusion. This model employs a Swin Transformer-based pre-trained module for feature extraction, which is the most effective module for modeling long-range dependencies. The fusion process of SWKIF-Fusion integrates the proposed knowledge measure of IFSs. IFSs inherently possess a high capability to handle uncertainty, and the knowledge measure of IFSs provides uncertainty quantification. This integration in the fusion phase mitigates the uncertainty in the decision-making process. This fusion of IFSs, knowledge measures, and the Swin Transformer-based deep learning model enhances the overall performance, as demonstrated through experiments on the TNO, Roadscene, OTCBVS, M3FD, and MSRS datasets. This study also fills the gap in developing knowledge measures for IFSs by proposing novel general construction methods. It presents a theoretically sound framework for knowledge measures of IFSs using well-established mathematical concepts such as t-norms, t-conorms, automorphisms, and aggregation operators.