Directional Adaptive Shuffle-Based Visual State-Space Models for Medical Image Restoration

Medical image restoration (MedIR) demands precise modeling of anisotropic spatial dependencies, where directional anatomical patterns are frequently degraded by conventional methods. We propose Directional Adaptive Shuffle Mamba (DASMamba), a state-space model architecture that addresses this challenge through two novel components: (1) the Directional Adaptive Shuffle Module (DASM), which captures long-range dependencies via directional adaptive random shuffle and selective scanning, and (2) the Dual-path Feedforward Network (DPFN), enhancing feature representation through multi-scale learning and dynamic channel fusion. By integrating these modules into a hierarchical U-shaped architecture, DASMamba achieves state-of-the-art performance on MRI super-resolution, CT denoising, and PET synthesis tasks while maintaining linear computational complexity. Our framework’s ability to preserve diagnostically critical structural details underscores its clinical value.