MAUGIF: Mechanism-Aware Unsupervised General Image Fusion via Dual Cross-Image Autoencoders

• URL: http://arxiv.org/abs/2511.08272v3
• Date: Fri, 14 Nov 2025 01:14:32 GMT
• Title: MAUGIF: Mechanism-Aware Unsupervised General Image Fusion via Dual Cross-Image Autoencoders
• Authors: Kunjing Yang, Zhiwei Wang, Minru Bai,
• Abstract summary: We propose a mechanism-aware unsupervised general image fusion (MAUGIF) method based on dual cross-image autoencoders.<n>We introduce a classification of additive and multiplicative fusion according to the inherent mechanisms of different fusion tasks.<n>The architecture of decoders varies according to their fusion mechanisms, enhancing both performance and interpretability.
• Score: 5.5579215593170685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image fusion aims to integrate structural and complementary information from multi-source images. However, existing fusion methods are often either highly task-specific, or general frameworks that apply uniform strategies across diverse tasks, ignoring their distinct fusion mechanisms. To address this issue, we propose a mechanism-aware unsupervised general image fusion (MAUGIF) method based on dual cross-image autoencoders. Initially, we introduce a classification of additive and multiplicative fusion according to the inherent mechanisms of different fusion tasks. Then, dual encoders map source images into a shared latent space, capturing common content while isolating modality-specific details. During the decoding phase, dual decoders act as feature injectors, selectively reintegrating the unique characteristics of each modality into the shared content for reconstruction. The modality-specific features are injected into the source image in the fusion process, generating the fused image that integrates information from both modalities. The architecture of decoders varies according to their fusion mechanisms, enhancing both performance and interpretability. Extensive experiments are conducted on diverse fusion tasks to validate the effectiveness and generalization ability of our method. The code is available at https://anonymous.4open.science/r/MAUGIF.

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