MFSFFuse: Multi-receptive Field Feature Extraction for Infrared and Visible Image Fusion Using Self-supervised Learning

The infrared and visible image fusion aims to fuse complementary information in different modalities to improve image quality and resolution, and facilitate subsequent visual tasks. Most of the current fusion methods suffer from incomplete feature extraction or redundancy, resulting in indistinctive targets or lost texture details. Moreover, the infrared and visible image fusion lacks ground truth, and the fusion results obtained by using unsupervised network training models may also cause the loss of important features. To solve these problems, we propose an infrared and visible image fusion method using self-supervised learning, called MFSFFuse. To overcome these challenges, we introduce a Multi-Receptive Field dilated convolution block that extracts multi-scale features using dilated convolutions. Additionally, different attention modules are employed to enhance information extraction in different branches. Furthermore, a specific loss function is devised to guide the optimization of the model to obtain an ideal fusion result. Extensive experiments show that, compared to the state-of-the-art methods, our method has achieved competitive results in both quantitative and qualitative experiments.