AB  - 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 dualbranch 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 Transformerbased SOTAs. Our results validate the effectiveness of frequencyaware state-space modeling for scalable and accurate medical image enhancement. Source code and dataset will be made publicly available.
AU  - Huang, W
AU  - Liao, X
AU  - Cao, W
AU  - Jia, W
AU  - Si, W
DA  - 2025/12/18
DO  - 10.1109/bibm66473.2025.11356215
EP  - 08
JO  - 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
PB  - Institute of Electrical and Electronics Engineers (IEEE)
PY  - 2025/12/18
SP  - 01
TI  - Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba
VL  - 00
Y1  - 2025/12/18
Y2  - 2026/05/26
ER  -