# Deform-Mamba Network for MRI Super-Resolution

arXiv: https://arxiv.org/abs/2407.05969 · HTML: https://mlsanity.com/paper/2407.05969/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 3 of 3)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)

## Built on by (most cited first, 17 of 17)

- [Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV](https://mlsanity.com/paper/2407.11087/) (2024)
- [Linear Attention Modeling for Learned Image Compression](https://mlsanity.com/paper/2502.05741/) (2025)
- [Mamba in Vision: A Comprehensive Survey of Techniques and Applications](https://mlsanity.com/paper/2410.03105/) (2024)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis](https://mlsanity.com/paper/2408.13074/) (2024)
- [Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution](https://mlsanity.com/paper/2504.10105/) (2025)
- [A Comprehensive Survey on Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2503.07097/) (2025)
- [ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation](https://mlsanity.com/paper/2503.19427/) (2025)
- [Mamba Based Feature Extraction And Adaptive Multilevel Feature Fusion For 3D Tumor Segmentation From Multi-modal Medical Image](https://mlsanity.com/paper/2504.21281/) (2025)
- [Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanning](https://mlsanity.com/paper/2512.19676/) (2025)
- [From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images](https://mlsanity.com/paper/2503.01347/) (2025)
- [RSRWKV: A Linear-Complexity 2D Attention Mechanism for Efficient Remote Sensing Vision Task](https://mlsanity.com/paper/2503.20382/) (2025)
- [Flip Distribution Alignment VAE for Multi-Phase MRI Synthesis](https://mlsanity.com/paper/2510.02970/) (2025)
- [FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction](https://mlsanity.com/paper/2512.03020/) (2025)
- [Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstruction](https://mlsanity.com/paper/2512.19095/) (2025)
- [Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselines](https://mlsanity.com/paper/2603.01449/) (2026)
- [MIAR: Medical Image Super-Resolution With Autoregressive Modeling](https://mlsanity.com/paper/2609.26103/) (2026)

## Abstract (arXiv)

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.