MVFusion: Generative Representation Learning With Masked Variational Autoencoders for Multi-Modality Image Fusion

Creating a comprehensively representative image while maintaining the merits of various modalities is a key focus of current Multi-Modality Image Fusion research. Existing unified methods often struggle to handle varying types of degradation while extracting modality-shared and modality-specific information from source images, leading to limitations in their generative or representation capabilities under different conditions. To address the challenge, we propose MVFusion, a novel self-supervised masked variational autoencoder framework that simultaneously enhances generative training and representation learning. It is designed to cope with varying image quality and dataset composition with a unified framework while ensuring effective fusion of modality information. Specifically, MVFusion employs a self-supervised masked autoencoder to reduce the impact of redundancy and degradation in the source images, and thus learns the latent distribution of degraded input images in the generative training stage. In addition, we incorporate variational feature learning to further preserve the distinctive modality features in the representation learning stage. Extensive experiments demonstrate that our model achieves promising results in several classical fusion tasks, including infrared-visible, multi-focus, multi-exposure, and medical image fusion. The code is available at https://github.com/shiboneng/MVFusion.