FusionMamba: dynamic feature enhancement for multimodal image fusion with Mamba

Abstract Multimodal image fusion aims to integrate information from different imaging techniques to produce a comprehensive, detail-rich single image for downstream vision tasks. Existing methods based on local convolutional neural networks (CNNs) struggle to capture global features efficiently, while Transformer-based models are computationally expensive, although they excel at global modeling. Mamba addresses these limitations by leveraging selective structured state space models (S4) to effectively handle long-range dependencies while maintaining linear complexity. In this paper, we propose FusionMamba, a novel dynamic feature enhancement framework that aims to overcome the challenges faced by CNNs and…

• NN
  National Natural Science Foundation of China

  Award: 62306061
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2023A1515140037