Fair and Faithful: A Diffusion-Enhanced Dataset and Hybrid State-Space Mamba for Face Super-Resolution

Face super-resolution (FSR) aims to enhance low-resolution facial im-ages into high-resolution versions. Existing FSR methods face significant chal-lenges, including the lack of publicly available and racially diverse datasets,which hinders reproducibility and subgroup-aware benchmarking. Additionally,many methods struggle to balance the restoration of fine-grained local detailswith the preservation of global facial structures, often producing results that areeither geometrically inconsistent or lack realistic details. To address these is-sues, we introduce SFHQFSR, a new FSR dataset offering high-quality, raciallydiverse facial images generated from generative models, addressing challengesin reproducibility, fairness, and dataset construction. Building on this, we pro-pose GLASNet, a hybrid state-space network that integrates global-local adaptivescanning with multi-domain refinement for high-quality facial image reconstruc-tion. GLASNet combines global semantic reasoning with region-specific localmodeling through a Region-Adaptive Scan Module and enhances restoration witha Multi-domain Refiner using channel, spatial, and frequency attention. Extensiveexperiments demonstrate that GLASNet achieves state-of-the-art performance.