Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba
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 ($<$0.75M), outperforming CNN-based and Transformer-based SOTAs. Our results validate the effectiveness of frequency-aware state-space modeling for scalable and accurate medical image enhancement.
Code (0)
등록된 구현이 없습니다.
Tasks
Medical Image EnhancementImage Super-ResolutionSimilar Papers 제목 키워드 기반
Medical image super-resolution method based on dense blended attention network
In order to address the issue that medical image would suffer from severe blurring caused by the lack of high-frequency details in the process of image super-resolution reconstruction, a novel medical image super-resolut…
Image Super-ResolutionSSIMSuper-ResolutionFlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation
Versatile audio super-resolution (SR) is the challenging task of restoring high-frequency components from low-resolution audio with sampling rates between 4kHz and 32kHz in various domains such as music, speech, and soun…
Audio Super-ResolutionSuper-ResolutionFrequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network
Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-…
Deep LearningGenerative Adversarial NetworkImage ReconstructionImage Super-Resolution+1Scale-Cascaded Diffusion Models for Super-Resolution in Medical Imaging
Diffusion models have been increasingly used as strong generative priors for solving inverse problems such as super-resolution in medical imaging. However, these approaches typically utilize a diffusion prior trained at …
Image Super-ResolutionDocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding
This work presents DocPedia, a novel large multimodal model (LMM) for versatile OCR-free document understanding, capable of parsing images up to 2,560$\times$2,560 resolution. Unlike existing work either struggle with hi…
document understandingLanguage ModelingLanguage ModellingLarge Language Model+1