  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.
