In this paper, we propose a self-prior guided Mamba-UNet network (SMamba-UNet) for medical image super-resolution. Existing methods are primarily based on convolutional neural networks (CNNs) or Transformers. CNNs-based methods fail to capture long-range dependencies, while Transformer-based approaches face heavy calculation challenges due to their quadratic computational complexity. Recently, State Space Models (SSMs) especially Mamba have emerged, capable of modeling long-range dependencies with linear computational complexity. Inspired by Mamba, our approach aims to learn the self-prior multi-scale contextual features under Mamba-UNet networks, which may help to super-resolve low-resolution medical images in an efficient way. Specifically, we obtain self-priors by perturbing the brightness inpainting of the input image during network training, which can learn detailed texture and brightness information that is beneficial for super-resolution. Furthermore, we combine Mamba with Unet network to mine global features at different levels. We also design an improved 2D-Selective-Scan (ISS2D) module to divide image features into different directional sequences to learn long-range dependencies in multiple directions, and adaptively fuse sequence information to enhance super-resolved feature representation. Both qualitative and quantitative experimental results demonstrate that our approach outperforms current state-of-the-art methods on two public medical datasets: the IXI and fastMRI.

我们提出了自先验引导的Mamba-UNet网络（SMamba-UNet）用于医学图像超分辨率。我们的方法使用Mamba来学习自先验多尺度上下文特征，结合Unet网络挖掘不同级别的全局特征，并使用改进的2D-Selective-Scan模块学习多个方向的长程依赖并自适应融合序列信息来提升超分辨率特征表示。定性和定量实验结果表明，我们的方法在两个公共医学数据集（IXI和fastMRI）上优于现有的最先进方法。

自优先导向的Mamba-UNet医学图像超分辨率网络