The field of multimodal image fusion and processing is moving towards more innovative and effective methods for combining complementary information from different image modalities. Recent developments focus on utilizing textual semantic information, implicit neural representations, and spectral-domain registration to improve the fusion process. These approaches aim to enhance the quality and informativeness of the fused images, making them more suitable for downstream tasks such as detection, segmentation, and classification. Noteworthy papers in this area include:

• TeSG, which introduces textual semantics to guide the image synthesis process, and
• INRFuse, which uses implicit neural representations to adaptively fuse features from infrared and visible light images. These advancements have the potential to significantly improve the performance of multimodal image fusion and processing applications, particularly in areas such as autonomous navigation and remote sensing.