SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution

State space models (SSMs) have emerged as a powerful paradigm for efficient single-image super-resolution (SR) due to their linear complexity and long-range modeling capabilities. However, existing Mamba-based methods typically rely on data-agnostic rigid scanning, which reshapes 2D images into 1D sequences over a fixed grid, inevitably disrupting spatial-semantic topology and introducing artifacts. Inspired by the $\mathbf{\text{Gestalt}}\mathbf{\text{perceptual}}\mathbf{\text{grouping}}\mathbf{\text{theory}}$, we propose $\mathbf{\text{SP-MoMamba}}$, a superpixel-driven mixture of state space experts designed for content-aware SR. Our core idea is to transform the traditional rigid scanning into a $\mathbf{\text{semantic-level}}\mathbf{\text{interaction}}$ by treating superpixels as fundamental units. Specifically, we introduce the $\mathbf{\text{Superpixel-driven}}\mathbf{\text{State}}\mathbf{\text{Space}}\mathbf{\text{Model}}\mathbf{\text{(SP-SSM)}}$, which compresses semantically homogeneous regions into high-order tokens to preserve global topological consistency. To address the conflict between fixed scanning scales and diverse semantic granularities, we develop the $\mathbf{\text{Multi-Scale}}\mathbf{\text{Superpixel}}\mathbf{\text{Mixture}}\mathbf{\text{of}}\mathbf{\text{State}}\mathbf{\text{Space}}\mathbf{\text{Experts}}\mathbf{\text{(MSS-MoE)}}$. This module utilizes a dynamic routing mechanism to adaptively assign scale-specific experts, effectively capturing multi-scale textures while reducing computational redundancy. Furthermore, to prevent the loss of high-frequency details during global abstraction, we introduce a $\mathbf{\text{Local}}\mathbf{\text{Spatial}}\mathbf{\text{Modulation}}\mathbf{\text{Expert}}\mathbf{\text{(LSME)}}$ to complement the global modeling, ensuring a precise reconstruction of sharp edges and fine structures. Extensive experiments on standard benchmarks demonstrate that SP-MoMamba achieves superior reconstruction fidelity and a more favorable efficiency-performance trade-off compared to state-of-the-art efficient SR methods.