Robotic surgical systems rely heavily on high-quality visual feedback for precise teleoperation; yet, surgical smoke from energy-based devices significantly degrades endoscopic video feeds, compromising the human-robot interface and surgical outcomes. This paper presents RGA-Net (Reciprocal Gating and Attention-fusion Network), a novel deep learning framework specifically designed for smoke removal in robotic surgery workflows. Our approach addresses the unique challenges of surgical smoke-including dense, non-homogeneous distribution and complex light scattering-through a hierarchical encoder-decoder architecture featuring two key innovations: (1) a Dual-Stream Hybrid Attention (DHA) module that combines shifted window attention with frequency-domain processing to capture both local surgical details and global illumination changes, and (2) an Axis-Decomposed Attention (ADA) module that efficiently processes multi-scale features through factorized attention mechanisms. These components are connected via reciprocal cross-gating blocks that enable bidirectional feature modulation between encoder and decoder pathways. Extensive experiments on the DesmokeData and LSD3K surgical datasets demonstrate that RGA-Net achieves superior performance in restoring visual clarity suitable for robotic surgery integration. Our method enhances the surgeon-robot interface by providing consistently clear visualization, laying a technical foundation for alleviating surgeons' cognitive burden, optimizing operation workflows, and reducing iatrogenic injury risks in minimally invasive procedures. These practical benefits could be further validated through future clinical trials involving surgeon usability assessments. The proposed framework represents a significant step toward more reliable and safer robotic surgical systems through computational vision enhancement.

翻译：机器人手术系统高度依赖高质量的视觉反馈以实现精确遥操作；然而，来自能量设备的术中烟雾会显著降低内窥镜视频质量，损害人机交互界面并影响手术效果。本文提出RGA-Net（互惠门控与注意力融合网络），这是一种专为机器人手术工作流中烟雾去除而设计的新型深度学习框架。我们的方法通过分层编码器-解码器架构应对术中烟雾的独特挑战——包括密集、非均匀分布和复杂的光散射效应——该架构具有两项关键创新：(1) 双流混合注意力（DHA）模块，结合移位窗口注意力与频域处理，以同时捕获局部手术细节和全局光照变化；(2) 轴分解注意力（ADA）模块，通过因子化注意力机制高效处理多尺度特征。这些组件通过互惠交叉门控块连接，实现编码器与解码器路径之间的双向特征调制。在DesmokeData和LSD3K手术数据集上的大量实验表明，RGA-Net在恢复适用于机器人手术集成的视觉清晰度方面取得了卓越性能。我们的方法通过提供持续清晰的视觉化效果，增强了外科医生-机器人交互界面，为减轻外科医生认知负荷、优化手术工作流程以及降低微创手术中医源性损伤风险奠定了技术基础。这些实际效益可通过未来涉及外科医生可用性评估的临床试验进一步验证。所提出的框架通过计算视觉增强，代表了迈向更可靠、更安全的机器人手术系统的重要一步。