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 Gestalt perceptual grouping theory, we propose 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 semantic-level interaction by treating superpixels as fundamental units. Specifically, we introduce the Superpixel-driven State Space Model (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 Multi-Scale Superpixel Mixture of State Space Experts (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 Local Spatial Modulation Expert (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.