Multi-modality image fusion is a technique that combines information from different sensors
or modalities enabling the fused image to retain complementary features from each modality …

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …

Recently, multi-modality scene perception tasks, eg, image fusion and scene understanding,
have attracted widespread attention for intelligent vision systems. However, early efforts …

Multi-modality image fusion and segmentation play a vital role in autonomous driving and
robotic operation. Early efforts focus on boosting the performance for only one task, eg …

Infrared and visible image fusion aims to integrate comprehensive information from multiple
sources to achieve superior performances on various practical tasks, such as detection, over …

Image fusion aims to integrate complementary characteristics of source images into a single
fused image that better serves human visual observation and machine vision perception …

This paper proposes a self-supervised framework based on a contrastive auto-encoding and
convolutional information exchange for multi-modal medical fusion tasks. It is well known …

This study proposes a novel unified and unsupervised end-to-end image fusion network,
termed as U2Fusion, which is capable of solving different fusion problems, including multi …

Infrared and visible image fusion targets to provide an informative image by combining
complementary information from different sensors. Existing learning-based fusion …

Infrared and visible image fusion aims to generate a fused image by integrating and
distinguishing complementary information from multiple sources. While the cross-attention …