用轻量级模型同时提升医学图像的全局结构与细节清晰度
Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba
- 引入频率感知门控机制，融合状态空间建模与多分支注意力
- 在五种医学影像上实现更高保真度，参数量低于0.75M
- 适合需要高效高精度图像增强的临床应用
医学图像超分辨率对提升诊断准确率、降低扫描成本和时间至关重要。然而，在计算开销低的前提下建模长程解剖结构与细粒度频率细节仍具挑战。本文提出FGMamba，一种新型频率感知门控状态空间模型，将全局依赖建模与细节增强统一于轻量架构。核心创新包括：GASM模块，结合高效状态空间建模与双分支空间-通道注意力；PFFM模块，通过FFT引导的多分辨率融合捕获高频细节。在超声、OCT、MRI、CT及内窥镜五种医学成像模态上的广泛评估表明，FGMamba在保持紧凑参数量（<0.75M）的同时，显著优于现有CNN与Transformer方法，实现了更高的PSNR/SSIM指标。结果验证了频率感知状态空间建模在可扩展、高精度医学图像增强中的有效性。
原文摘要 · Abstract (English)
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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