针对遥感图像生成，提出高效无训练的分辨率提升方法。
SHARP: Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis
- 基于频谱感知调度器动态调整位置编码频率
- 在多个分辨率上均达到领先性能，计算开销低于4%
- 适合需要高精度结构保留的遥感图像生成任务
遥感图像的文本到图像合成缺乏可访问、高性能的生成基础，因直接在大尺寸高分辨率上训练扩散模型计算成本过高。通过旋转位置编码（RoPE）外推实现无训练分辨率提升是一种高效替代方案，但现有方法采用适用于自然场景的静态缩放规则，而遥感图像以密集微小目标为主，依赖高频结构完整性。本文提出完整框架用于大规模遥感合成：首先构建超过10万张遥感图文对，训练得到领域专用生成先验RS-FLUX；其次提出 extbf{SHARP}，一种无训练外推算法，引入理性衰减调度器，在去噪过程中调节RoPE频率：早期强位置外推保证全局布局连贯性，后期逐步放松以恢复密集高频细节。大量实验表明，SHARP在多个提升分辨率下均保持最先进性能，计算开销低于4%，为大规模遥感生成提供了高效且结构忠实的解决方案。
原文摘要 · Abstract (English)
Text-to-image synthesis for remote sensing (RS) lacks an accessible, high-performance generative foundation, as directly training diffusion models at large, high resolutions is computationally prohibitive. Training-free resolution promotion via Rotary Position Embedding (RoPE) extrapolation offers an efficient alternative, but existing algorithms apply static scaling rules tailored to natural scenes, whereas RS imagery is dominated by dense, minute instances that hinge on high-frequency structural integrity. We present a comprehensive framework for large-scale RS synthesis. First, we curate over 100{,}000 image-text pairs to train RS-FLUX, a domain-specialized generative prior. Second, we propose \textbf{SHARP}, a training-free extrapolation algorithm that introduces a Rational Decay Scheduler to modulate RoPE frequencies throughout denoising: strong positional extrapolation early on enforces coherent global layouts, and progressive relaxation later recovers dense high-frequency details. Extensive experiments show that SHARP consistently achieves state-of-the-art performance across multiple promoted resolutions with negligible ($<$4\%) overhead, offering an efficient and structurally faithful solution for large-scale RS generation.
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