Abstract
Surgical smoke severely degrades intraoperative video quality, obscuring anatomical structures and limiting surgical perception. Existing learning-based desmoking approaches rely on scarce paired supervision and deterministic restoration pipelines, making it difficult to perform exploration or reinforcement-driven refinement under real surgical conditions. We propose PhySe-RPO, a diffusion restoration framework optimized through Physics- and Semantics-Guided Relative Policy Optimization. The core idea is to transform deterministic restoration into a stochastic policy, enabling trajectory-level exploration and critic-free updates via group-relative optimization. A physics-guided reward imposes illumination and color consistency, while a visual-concept semantic reward learned from CLIP-based surgical concepts promotes smoke-free and anatomically coherent restoration. Together with a reference-free perceptual constraint, PhySe-RPO produces results that are physically consistent, semantically faithful, and clinically interpretable across synthetic and real robotic surgical datasets, providing a principled route to robust diffusion-based restoration under limited paired supervision.
Abstract (translated)
手术烟雾会严重降低术中视频质量，遮蔽解剖结构并限制手术感知。现有的基于学习的去烟雾方法依赖于稀缺的配对监督和确定性恢复流程，难以在真实手术条件下进行探索或基于强化的精细调整。我们提出了PhySe-RPO，一个通过物理与语义引导的相对策略优化进行优化的扩散式恢复框架。其核心思想是将确定性恢复转化为随机策略，从而通过组相对优化支持轨迹级探索和无评判器更新。物理引导奖励施加光照与颜色一致性约束，而从基于CLIP的手术概念中学习的视觉概念语义奖励则促进无烟雾且解剖连贯的恢复。结合无参考感知约束，PhySe-RPO在合成与真实机器人手术数据集上生成了物理一致、语义忠实且临床可解释的结果，为在有限配对监督下实现稳健的扩散式恢复提供了原则性路径。
URL
https://arxiv.org/abs/2603.22844
