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arXiv Computer Vision By Wenbin Zou, Yawen Cui, Yi Wang, Lap-Pui Chau, Liang Chen, Jinshan Pan, Huiping Zhuang, Guanbin Li

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

Read the original on arXiv Computer Vision →

SP-MoMamba introduces a superpixel-driven mixture of state space experts for efficient image super‑resolution. By grouping spatially coherent features into region‑level tokens, the Superpixel‑SSM performs global sequence modeling over compact representations, reducing redundant computation while enabling long‑range structural interaction. The Multi‑Scale Superpixel Mixture of State Space Experts further adapts to varying representation granularities, and a Local Spatial Modulation Expert refines local high‑frequency details, resulting in strong reconstruction performance with a favorable trade‑off among model size, computational cost, and inference efficiency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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