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

• URL: http://arxiv.org/abs/2510.27296v1
• Date: Fri, 31 Oct 2025 09:12:12 GMT
• Title: Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba
• Authors: Wenfeng Huang, Xiangyun Liao, Wei Cao, Wenjing Jia, Weixin Si,
• Abstract summary: We propose FGMamba, a novel frequency-aware gated state-space model that unifies global dependency modeling and fine-detail enhancement into a lightweight architecture.<n>Our results validate the effectiveness of frequency-aware state-space modeling for scalable and accurate medical image enhancement.
• Score: 10.69081892501522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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