SFINet: A semantic feature interactive learning network for full-time infrared and visible image fusion

Abstract

Infrared and visible image fusion aims to combine data from various source images to generate a high-quality image. Nevertheless, numerous fusion methods often prioritize visual quality above semantic information. To address this problem, we present a Semantic Feature Interactive Learning Network (SFINet) for full-time infrared and visible images. The SFINet encompasses an image fusion network and an image segmentation network through a Semantic Feature Interaction (SFI) module. The image fusion network employs Multi-scale Feature Extraction (MFE) modules to capture global and local information at multiple scales. Meanwhile, it performs an adaptive fusion of complementary information using a Dual Attention Feature Fusion (DAFF) module. The image segmentation network guides the image fusion network using the SFI module for semantic feature interaction. Comparative results prove that the proposed method is superior to state-of-the-art (SOTA) models in image fusion and semantic segmentation tasks. The code is available at https://github.com/songwenhao123/SFINet.