Robot-assisted surgery has profoundly influenced current forms of minimally invasive surgery. However, in transurethral suburethral urological surgical robots, they need to work in a liquid environment. This causes vaporization of the liquid when shearing and heating is performed, resulting in bubble atomization that affects the visual perception of the robot. This can lead to the need for uninterrupted pauses in the surgical procedure, which makes the surgery take longer. To address the atomization characteristics of liquids under urological surgical robotic vision, we propose an unsupervised zero-shot dehaze method (RSF-Dehaze) for urological surgical robotic vision. Specifically, the proposed Region Similarity Filling Module (RSFM) of RSF-Dehaze significantly improves the recovery of blurred region tissues. In addition, we organize and propose a dehaze dataset for robotic vision in urological surgery (USRobot-Dehaze dataset). In particular, this dataset contains the three most common urological surgical robot operation scenarios. To the best of our knowledge, we are the first to organize and propose a publicly available dehaze dataset for urological surgical robot vision. The proposed RSF-Dehaze proves the effectiveness of our method in three urological surgical robot operation scenarios with extensive comparative experiments with 20 most classical and advanced dehazing and image recovery algorithms. The proposed source code and dataset are available at https://github.com/wurenkai/RSF-Dehaze .

翻译：机器人辅助手术已深刻影响当前微创手术的形式。然而，在经尿道尿道下泌尿外科手术机器人中，其需在液体环境中工作。这导致在剪切与加热过程中液体汽化，产生气泡雾化现象，进而影响机器人的视觉感知。此现象可能引致手术过程中需要不间断地暂停操作，从而延长手术时间。为应对泌尿外科手术机器人视觉下液体的雾化特性，我们提出一种用于泌尿外科手术机器人视觉的无监督零样本去雾方法（RSF-Dehaze）。具体而言，所提出的RSF-Dehaze中的区域相似性填充模块（RSFM）显著提升了模糊区域组织的复原效果。此外，我们整理并提出了一个面向泌尿外科手术机器人视觉的去雾数据集（USRobot-Dehaze数据集）。特别地，该数据集涵盖了三种最常见的泌尿外科手术机器人操作场景。据我们所知，我们是首个整理并公开面向泌尿外科手术机器人视觉去雾数据集的研究。通过20种最经典与先进的去雾及图像复原算法的广泛对比实验，所提出的RSF-Dehaze在三种泌尿外科手术机器人操作场景中验证了我们方法的有效性。所提出的源代码与数据集已公开于 https://github.com/wurenkai/RSF-Dehaze 。