mlsanityData to 7 Oct 2026

Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba

arXiv 2510.27296 ↗First posted 31 Oct 2025cs.CVcited by 0

On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis

Abstract (arXiv)

Medical image super-resolution (SR) is essential for enhancing diagnostic accuracy while reducing acquisition cost and scanning time. However, modeling both long-range anatomical structures and fine-grained frequency details with low computational overhead remains challenging. We propose FGMamba, a novel frequency-aware gated state-space model that unifies global dependency modeling and fine-detail enhancement into a lightweight architecture. Our method introduces two key innovations: a Gated Attention-enhanced State-Space Module (GASM) that integrates efficient state-space modeling with dual-branch spatial and channel attention, and a Pyramid Frequency Fusion Module (PFFM) that captures high-frequency details across multiple resolutions via FFT-guided fusion. Extensive evaluations across five medical imaging modalities (Ultrasound, OCT, MRI, CT, and Endoscopic) demonstrate that FGMamba achieves superior PSNR/SSIM while maintaining a compact parameter footprint (<0.75M), outperforming CNN-based and Transformer-based SOTAs. Our results validate the effectiveness of frequency-aware state-space modeling for scalable and accurate medical image enhancement.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 21 of its 40 references that are in the map. First posted on arXiv 31 Oct 2025.

Builds on

21 of 40 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model6 Sep

Other references · 20

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  2. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  3. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  4. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  5. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  6. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  7. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
  8. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 349
  9. 2018Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Networkcited by 263
  10. 2021Transformer for Single Image Super-Resolutioncited by 150
  11. 2024Demystify Mamba in Vision: A Linear Attention Perspectivecited by 101
  12. 2024Mamba-ND: Selective State Space Modeling for Multi-Dimensional Datacited by 84
8 more
  1. 2024Theoretical Foundations of Deep Selective State-Space Modelscited by 73
  2. 2024Frequency-Assisted Mamba for Remote Sensing Image Super-Resolutioncited by 47
  3. 2024QMambaBSR: Burst Image Super-Resolution with Query State Space Modelcited by 27
  4. 2022Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformercited by 22
  5. 2022Cross-Modality High-Frequency Transformer for MR Image Super-Resolutioncited by 20
  6. 2020Lightweight image super-resolution with enhanced CNNcited by 15
  7. 2023Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRIcited by 10
  8. 2024DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Modelscited by 8

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Not cited yet.

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  1. 2025Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution
  2. 2025Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanning
  3. 2024Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution
  4. 2025Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution
  5. 2025SpectMamba: Integrating Frequency and State Space Models for Enhanced Medical Image Detection
  6. 2025FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation
  7. 2024I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling
  8. 2024Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis