Self-supervised Multiplex Consensus Mamba for General Image Fusion

• URL: http://arxiv.org/abs/2512.20921v1
• Date: Wed, 24 Dec 2025 03:57:21 GMT
• Title: Self-supervised Multiplex Consensus Mamba for General Image Fusion
• Authors: Yingying Wang, Rongjin Zhuang, Hui Zheng, Xuanhua He, Ke Cao, Xiaotong Tu, Xinghao Ding,
• Abstract summary: We propose SMC-Mamba, a Self-supervised Multiplex Consensus Mamba framework for general image fusion.<n> Modality-Agnostic Feature Enhancement (MAFE) module preserves fine details through adaptive gating.<n>Cross-modal scanning within MCCM strengthens feature interactions across modalities.<n>Bi-level Self-supervised Contrastive Learning Loss (BSCL) preserves high-frequency information without increasing computational overhead.
• Score: 34.041756423040184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image fusion integrates complementary information from different modalities to generate high-quality fused images, thereby enhancing downstream tasks such as object detection and semantic segmentation. Unlike task-specific techniques that primarily focus on consolidating inter-modal information, general image fusion needs to address a wide range of tasks while improving performance without increasing complexity. To achieve this, we propose SMC-Mamba, a Self-supervised Multiplex Consensus Mamba framework for general image fusion. Specifically, the Modality-Agnostic Feature Enhancement (MAFE) module preserves fine details through adaptive gating and enhances global representations via spatial-channel and frequency-rotational scanning. The Multiplex Consensus Cross-modal Mamba (MCCM) module enables dynamic collaboration among experts, reaching a consensus to efficiently integrate complementary information from multiple modalities. The cross-modal scanning within MCCM further strengthens feature interactions across modalities, facilitating seamless integration of critical information from both sources. Additionally, we introduce a Bi-level Self-supervised Contrastive Learning Loss (BSCL), which preserves high-frequency information without increasing computational overhead while simultaneously boosting performance in downstream tasks. Extensive experiments demonstrate that our approach outperforms state-of-the-art (SOTA) image fusion algorithms in tasks such as infrared-visible, medical, multi-focus, and multi-exposure fusion, as well as downstream visual tasks.

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