4 accepted papers
3D medical image fusion (MIF) and segmentation (MIS) are critical and inherently synergistic tasks in medical image analysis. However, fundamentally integrating them remains highly challenging, since effective collaborative paradigms are still scarce and their optimization objectives fundamentally d
Image Fusion (IF) aims to integrate complementary features from multiple source images into a single image. However, a key challenge in this field is the lack of large-scale real-world training datasets. Existing models typically rely on either small datasets or synthetic, less realistic datasets. T
Infrared and visible image fusion (VIS-IR) aims to integrate complementary information from both source images to produce a fused image with enriched details. However, most existing fusion models lack controllability, making it difficult to customize the fused output according to user preferences. T…
Medical image fusion (MIF) aims to extract complementary features from multi-modal source images and fuse them into a single image to assist in clinical diagnostics. Despite its importance, MIF faces two primary challenges: the lack of tailored paradigms for CMSF extraction and insufficient dual exp…