发表机构
Korea Advanced Institute of Science and Technology（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有SAR超分辨率模型难以保留物理特性的问题，提出ProSR模型，通过语义引导的离散令牌预测等技术，在Umbra数据集上实现了兼顾视觉质量与散射特性的超分辨率效果。
AI 中文摘要
高分辨率合成孔径雷达（SAR）影像对自动目标识别等精准分析至关重要，但其获取成本高昂。尽管生成式图像超分辨率（ISR）模型提供了颇具前景的替代方案，但当前基于平滑近似的扩散框架往往难以保留相干散射统计特性，引发随机结构失真，与真实SAR物理特性一致性较差。为解决这一问题，我们提出语义原型引导超分辨率（ProSR），将SAR ISR重新表述为量化隐空间内语义引导的离散令牌预测任务。通过将信号特征映射至离散散射基元，ProSR在不过度平滑的前提下保留了SAR的脉冲特性。此外，我们将自监督学习主干网络集成至SAR ISR中，以提取无标签语义先验，克服标签稀缺问题。在这些先验的引导下，我们引入语义对齐细节编码，将高频信号解耦为离散散射基元。与此同时，语义原型图生成器显式构建语义原型图，使原型图引导注意力机制能够在相同类别内传递信息流并减轻类间干扰。为验证我们的方法，我们展示了来自Umbra开放数据集的大规模0.25米分辨率基准测试。实验结果表明，ProSR在实现卓越视觉质量的同时，保留了实际SAR应用所需的关键散射特性。
英文摘要
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.
CommentsAccepted to ECCV 2026
