Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion
<|reference_start|>Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion: Multi-modal image fusion integrates complementary information from different modalities to produce enhanced and informative images. Although State-Space Models, such as Mamba, are proficient in long-range modeling with linear complexity, most Mamba-based approaches use fixed scanning strategies, which can introduce biased prior information. To mitigate this issue, we propose a novel Bayesian-inspired scanning strategy called Random Shuffle, supplemented by an theoretically-feasible inverse shuffle to maintain information coordination invariance, aiming to eliminate biases associated with fixed sequence scanning. Based on this transformation pair, we customized the Shuffle Mamba Framework, penetrating modality-aware information representation and cross-modality information interaction across spatial and channel axes to ensure robust interaction and an unbiased global receptive field for multi-modal image fusion. Furthermore, we develop a testing methodology based on Monte-Carlo averaging to ensure the model's output aligns more closely with expected results. Extensive experiments across multiple multi-modal image fusion tasks demonstrate the effectiveness of our proposed method, yielding excellent fusion quality over state-of-the-art alternatives. Code will be available upon acceptance.<|reference_end|>
@article{cao2024shuffle,
    title={Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal
  Image Fusion},
    author={Ke Cao, Xuanhua He, Tao Hu, Chengjun Xie, Jie Zhang, Man Zhou, Danfeng
  Hong},
    journal={arXiv preprint arXiv:2409.01728},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01728},
    primaryClass={cs.CV}
}