During laparoscopic surgery, smoke generated by tissue cauterization can significantly degrade the visual quality of endoscopic frames, increasing the risk of surgical errors and hindering both clinical decision-making and computer-assisted visual analysis. Consequently, removing surgical smoke is critical to ensuring patient safety and maintaining operative efficiency. In this study, we propose the Surgical Atmospheric Model (SurgiATM) for surgical smoke removal. SurgiATM statistically bridges a physics-based atmospheric model and data-driven deep learning models, combining the superior generalizability of the former with the high accuracy of the latter. Furthermore, SurgiATM is designed as a lightweight, plug-and-play module that can be seamlessly integrated into diverse surgical desmoking architectures to enhance their accuracy and stability, better meeting clinical requirements. It introduces only two hyperparameters and no additional trainable weights, preserving the original network architecture with minimal computational and modification overhead. We conduct extensive experiments on three public surgical datasets with ten desmoking methods, involving multiple network architectures and covering diverse procedures, including cholecystectomy, partial nephrectomy, and diaphragm dissection. The results demonstrate that incorporating SurgiATM commonly reduces the restoration errors of existing models and relatively enhances their generalizability, without adding any trainable layers or weights. This highlights the convenience, low cost, effectiveness, and generalizability of the proposed method. The code for SurgiATM is released at https://github.com/MingyuShengSMY/SurgiATM.

翻译：在腹腔镜手术中，组织烧灼产生的烟雾会显著降低内窥镜图像的视觉质量，增加手术错误风险，并阻碍临床决策和计算机辅助视觉分析。因此，去除手术烟雾对于确保患者安全和维持手术效率至关重要。本研究提出用于手术烟雾去除的手术大气模型（SurgiATM）。SurgiATM在统计学上桥接了基于物理的大气模型和数据驱动的深度学习模型，结合了前者优越的泛化能力和后者的高精度。此外，SurgiATM被设计为一个轻量级、即插即用的模块，可以无缝集成到多种手术去烟雾架构中，以提高其准确性和稳定性，更好地满足临床需求。它仅引入两个超参数，不增加任何可训练权重，以最小的计算和修改开销保持了原始网络架构。我们在三个公开手术数据集上，针对十种去烟雾方法进行了广泛实验，涉及多种网络架构，并覆盖了包括胆囊切除术、部分肾切除术和膈肌解剖在内的多种手术程序。结果表明，集成SurgiATM通常能降低现有模型的恢复误差，并相对增强其泛化能力，且无需添加任何可训练层或权重。这凸显了所提方法的便捷性、低成本、有效性和泛化性。SurgiATM的代码发布于 https://github.com/MingyuShengSMY/SurgiATM。