Deform-Mamba Network for MRI Super-Resolution
<|reference_start|>Deform-Mamba Network for MRI Super-Resolution: In this paper, we propose a new architecture, called Deform-Mamba, for MR image super-resolution. Unlike conventional CNN or Transformer-based super-resolution approaches which encounter challenges related to the local respective field or heavy computational cost, our approach aims to effectively explore the local and global information of images. Specifically, we develop a Deform-Mamba encoder which is composed of two branches, modulated deform block and vision Mamba block. We also design a multi-view context module in the bottleneck layer to explore the multi-view contextual content. Thanks to the extracted features of the encoder, which include content-adaptive local and efficient global information, the vision Mamba decoder finally generates high-quality MR images. Moreover, we introduce a contrastive edge loss to promote the reconstruction of edge and contrast related content. Quantitative and qualitative experimental results indicate that our approach on IXI and fastMRI datasets achieves competitive performance.<|reference_end|>
@article{ji2024deform-mamba,
    title={Deform-Mamba Network for MRI Super-Resolution},
    author={Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan},
    journal={arXiv preprint arXiv:2407.05969},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05969},
    primaryClass={cs.CV}
}