SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution
State space models (SSMs) have emerged as an efficient paradigm for single-image super-resolution (SR) due to their linear complexity and long-range modeling capabilities. However, existing visual SSMs mainly focus on improving how densely represented image features are traversed, while the construction of the visual sequence itself remains largely tied to predefined spatial layouts. Inspired by Gestalt perceptual grouping, we propose SP-MoMamba, a superpixel-driven mixture of state space experts for efficient SR. Instead of performing state-space modeling over densely serialized pixel features, the proposed Superpixel-driven State Space Model (SP-SSM) organizes spatially coherent feature...
| Authors | Wenbin Zou, Yawen Cui, Yi Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 May 2026 |
| Last updated at source | 18 September 2026 |
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