ProSR通过语义原型引导离散建模，提升雷达超分辨率图像物理一致性。
ProSR: Semantic-Prototype-Guided Discrete Modeling for Physically Consistent SAR Super-Resolution
- 将SAR超分辨重构为量化潜空间中的离散标记预测任务。
- 在0.25米分辨率数据集上实现视觉质量与散射特性双重提升。
- 适合需要高物理一致性的雷达图像分析场景使用。
高分辨率合成孔径雷达（SAR）图像是实现自动目标识别等精密分析的关键，但其获取成本高昂。尽管生成式图像超分辨率（ISR）模型提供了可行替代方案，现有基于平滑近似的扩散框架常因无法保持相干散射统计特性，导致随机结构失真，与真实SAR物理规律不符。为此，本文提出语义原型引导的超分辨率方法（ProSR），将SAR ISR重新构架为量化潜空间内的语义引导离散标记预测任务。通过将信号特征映射至离散散射基元，ProSR有效保留了SAR固有的脉冲特性，避免过度平滑。进一步地，引入自监督学习骨干网络以提取无标签语义先验，缓解标签稀缺问题。在此基础上，设计语义对齐细节编码模块，将高频信号解耦为离散散射基元。同时，语义原型图生成器显式构建语义原型图，支持原型图引导注意力机制，在同类间路由信息流并抑制跨类干扰。为验证方法有效性，我们基于Umbra Open Dataset构建了大规模0.25米分辨率基准数据集。实验结果表明，ProSR在保持关键散射特性的同时，显著提升视觉质量，满足实际SAR应用需求。
原文摘要 · Abstract (English)
High-resolution Synthetic Aperture Radar (SAR) imagery is 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 scattering statistics, causing stochastic structural distortions that are less consistent with real SAR physics. To address this, we propose Semantic Prototype-Guided Super-Resolution (ProSR), reformulating SAR ISR as a semantically-guided discrete token prediction task within a quantized latent 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 into SAR ISR to extract label-free semantic priors, overcoming label scarcity. Guided by these priors, we introduce Semantic-Aligned Detail Encoding to decouple high-frequency signals into discrete scattering primitives. In parallel, the Semantic Prototype Map Generator explicitly constructs semantic prototype maps, allowing Prototype-Map-Guided Attention to route the information flows within identical categories and mitigate inter-class interference. To validate our approach, we present a large-scale 0.25m resolution benchmark from the Umbra Open Dataset. Experimental results show ProSR achieves superior visual quality while preserving essential scattering characteristics required for practical SAR applications.
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