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

TL;DR

FGMamba is a lightweight, frequency-aware model that significantly improves medical image super-resolution by combining global dependency modeling and fine-detail enhancement across multiple modalities.

Contribution

The paper introduces FGMamba, a novel frequency-aware gated state-space model with dual-branch attention and FFT-guided fusion, advancing medical image super-resolution techniques.

Findings

Achieves superior PSNR/SSIM across five medical imaging modalities.

Maintains a parameter count of less than 0.75 million, ensuring efficiency.

Outperforms CNN-based and Transformer-based state-of-the-art methods.

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.