Directional Adaptive Shuffle-Based Visual State-Space Models for Medical Image Restoration (MICCAI 2025 Oral)

Note: This repository is under active preparation. Full code and introduction will be uploaded by the end of next week.

Abstract:
Medical image restoration (MedIR) demands precise modeling of anisotropic spatial dependencies, where directional anatomical patterns are frequently degraded by conventional methods. We propose Directional Adaptive Shuffle Mamba (DASMamba), a state-space model architecture that addresses this challenge through two novel components: (1) the Directional Adaptive Shuffle Module (DASM), which captures long-range dependencies via directional adaptive random shuffle and selective scanning, and (2) the Dual-path Feedforward Network (DPFN), enhancing feature representation through multi-scale learning and dynamic channel fusion. By integrating these modules into a hierarchical U-shaped architecture, DASMamba achieves state-of-the-art performance on MRI super-resolution, CT denoising, and PET synthesis tasks while maintaining linear computational complexity. Our framework’s ability to preserve diagnostically critical structural details underscores its clinical value.

Training

Testing

Pre-trained Models

Dataset	CT	MRI	PET
Google Drive	Download	Download	Download

Downloadable Results

Dataset	CT	MRI	PET
Google Drive	Download	Download	Download

Visual results

Acknowledgment

This repository builds upon the code and dataset provided by the authors of Restore-RWKV.
We thank them for making their work publicly available. Thanks to the authors for their excellent work.

Citation

If you use this repository or ideas from our work in your research, please consider cite both our paper and Restore-RWKV:

@misc{yang2024restorerwkv,
  title={Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV},
  author={Zhiwen Yang and Jiayin Li and Hui Zhang and Dan Zhao and Bingzheng Wei and Yan Xu},
  year={2024},
  eprint={2407.11087},
  archivePrefix={arXiv},
  primaryClass={eess.IV}
}

@InProceedings{ChaSim_Directional_MICCAI2025,
        author = { Chan, Simon C. K. AND Shi, Lulin AND Huang, Bingxin AND Wong, Terence T. W.},
        title = { { Directional Adaptive Shuffle-Based Visual State-Space Models for Medical Image Restoration } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
        year = {2025},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15972},
        month = {September},
        page = {160 -- 170}
}