ProSR: Semantic-Prototype-Guided Discrete Modeling for Physically Consistent SAR Super-Resolution

High-resolution Synthetic Aperture Radar (SAR) imageryis critical for precision analysis such as automatic target recognition,yet its acquisition is costly. Although generative image super-resolution(ISR) models offer a promising alternative, current smooth-approximation-based diffusion frameworks often struggle to preserve the coherent scat-tering statistics, causing stochastic structural distortions that are lessconsistent with real SAR physics. To address this, we propose Semantic-Prototype-Guided Super-Resolution (ProSR), reformulating SAR ISR asa semantically-guided discrete token prediction task within a quantizedlatent space. By mapping signal features to discrete scattering primitives,ProSR preserves the impulsive nature of SAR without over-smoothing.Furthermore, we integrate a Self-Supervised Learning backbone intoSAR ISR to extract label-free semantic priors, overcoming label scarcity.Guided by these priors, we introduce Semantic-Aligned Detail Encodingto decouple high-frequency signals into discrete scattering primitives. Inparallel, the Semantic Prototype Map Generator explicitly constructssemantic prototype maps, allowing Prototype-Map-Guided Attentionto route the information flows within identical categories and mitigateinter-class interference. To validate our approach, we present a large-scale0.25 m resolution benchmark from the Umbra Open Dataset. Experimen-tal results show ProSR achieves superior visual quality while preservingessential scattering characteristics required for practical SAR applications.