Abstract
Multi-modal medical image fusion (MMIF) aims to integrate complementary information from different modalities to obtain a fused image that contains more comprehensive details, providing clinicians with a more thorough reference for diagnosis. However, most existing deep learning-based fusion methods predominantly focus on the local statistical features within images, which limits the ability of the model to capture long-range dependencies and correlations within source images, thus compromising fusion performance. To address this issue, we propose an unsupervised image fusion method guided by stochastic structural similarity (S3IMFusion). This method incorporates a multi-scale fusion network based on CNN and Transformer modules to extract complementary information from the images effectively. During the training, a loss function with the ability to interact global contextual information was designed. Specifically, a random sorting index is generated based on the source images, and pixel features are mixed and rearranged between the fused and source images according to this index. The structural similarity loss is then computed by averaging the losses between pixel blocks of the rearranged images. This ensures that the fusion result preserves the globally correlated complementary features from the source images. Experimental results on the Harvard dataset demonstrate that S3IMFusion outperforms existing methods, achieving more accurate fusion of medical images. Additionally, we extend the method to infrared and visible image fusion tasks, with results indicating that S3IMFusion exhibits excellent generalization performance.