  Image fusion aims to generate a high-resolution multi/hyper-spectral image by
combining a high-resolution image with limited spectral information and a
low-resolution image with abundant spectral data. Current deep learning
(DL)-based methods for image fusion primarily rely on CNNs or Transformers to
extract features and merge different types of data. While CNNs are efficient,
their receptive fields are limited, restricting their capacity to capture
global context. Conversely, Transformers excel at learning global information
but are hindered by their quadratic complexity. Fortunately, recent
advancements in the State Space Model (SSM), particularly Mamba, offer a
promising solution to this issue by enabling global awareness with linear
complexity. However, there have been few attempts to explore the potential of
the SSM in information fusion, which is a crucial ability in domains like image
fusion. Therefore, we propose FusionMamba, an innovative method for efficient
image fusion. Our contributions mainly focus on two aspects. Firstly,
recognizing that images from different sources possess distinct properties, we
incorporate Mamba blocks into two U-shaped networks, presenting a novel
architecture that extracts spatial and spectral features in an efficient,
independent, and hierarchical manner. Secondly, to effectively combine spatial
and spectral information, we extend the Mamba block to accommodate dual inputs.
This expansion leads to the creation of a new module called the FusionMamba
block, which outperforms existing fusion techniques such as concatenation and
cross-attention. We conduct a series of experiments on five datasets related to
three image fusion tasks. The quantitative and qualitative evaluation results
demonstrate that our method achieves SOTA performance, underscoring the
superiority of FusionMamba. The code is available at
https://github.com/PSRben/FusionMamba.
