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.

提出了一种名为Deform-Mamba的新架构，用于MR图像超分辨率。与传统的基于CNN或Transformer的超分辨率方法相比，我们的方法旨在有效地探索图像的局部和全局信息，并引入了对比度边缘损失以促进边缘和对比度有关内容的重建。定量和定性实验结果表明，我们的方法在IXI和fastMRI数据集上取得了竞争性能。

MRI超分辨率的Deform-Mamba网络