Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba
This work addresses the problem of efficient and accurate medical image enhancement for diagnostic applications, representing a strong specific gain with novel method innovations.
The paper tackled the challenge of modeling long-range anatomical structures and fine-grained frequency details in medical image super-resolution with low computational overhead, and proposed FGMamba, which achieved superior PSNR/SSIM across five medical imaging modalities while maintaining under 0.75M parameters.
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.