内窥镜手术图像复原基准评测与展望
Benchmarking Endoscopic Surgical Image Restoration and Beyond
提供了真实手术场景下多类型退化（烟雾、雾化、飞溅）的配对图像复原数据集与标准化评测基准，可直接用于训练和评估面向医疗内窥镜的复原算法，并揭示了手术场景与自然场景在结构感知和语义理解上的退化差异，为领域特定复原方法设计提供依据。
原文依据 we introduce a real-world open-source surgical image restoration dataset covering laparoscopic environments, called SurgClean
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在内窥镜手术中，清晰且高质量的视野对于外科医生做出准确的术中决策至关重要。然而，持续存在的视觉退化，包括能量设备产生的烟雾、热梯度导致的镜头雾化以及手术过程中血液或组织液飞溅造成的镜头污染，严重损害了视觉清晰度。这些退化会严重阻碍手术流程并对患者安全构成风险。为了系统地研究和解决各种形式的手术场景退化，我们引入了一个真实世界的开源手术图像复原数据集，涵盖腹腔镜环境，称为SurgClean，它涉及来自两个医疗站点的多类型图像复原任务，即去烟、去雾和去飞溅。SurgClean包含3,113张图像，具有多样的退化类型和相应的配对参考标签。基于SurgClean，我们建立了一个标准化的评估基准，并提供了22种代表性通用任务特定图像复原方法的性能，包括12种通用方法和10种任务特定图像复原方法。实验结果表明，相对于临床需求存在显著性能差距，突显了智能手术复原算法进步的关键机会。此外，我们从结构感知和语义理解的角度探索了手术场景与自然场景之间的退化差异，为领域特定的图像复原研究提供了基础见解。我们的工作旨在赋能复原算法并提高临床流程的效率。资源可访问：https://github.com/PJLallen/Surgical-Image-Restoration。
英文原文摘要 1757 字符
In endoscopic surgery, a clear and high-quality visual field is critical for surgeons to make accurate intraoperative decisions. However, persistent visual degradation, including smoke generated by energy devices, lens fogging from thermal gradients, and lens contamination due to blood or tissue fluid splashes during surgical procedures, severely impairs visual clarity. These degenerations can seriously hinder surgical workflow and pose risks to patient safety. To systematically investigate and address various forms of surgical scene degradation, we introduce a real-world open-source surgical image restoration dataset covering laparoscopic environments, called SurgClean, which involves multi-type image restoration tasks from two medical sites, i.e., desmoking, defogging, and desplashing. SurgClean comprises 3,113 images with diverse degradation types and corresponding paired reference labels. Based on SurgClean, we establish a standardized evaluation benchmark and provide performance for 22 representative generic task-specific image restoration approaches, including 12 generic and 10 task-specific image restoration approaches. Experimental results reveal substantial performance gaps relative to clinical requirements, highlighting a critical opportunity for algorithm advancements in intelligent surgical restoration. Furthermore, we explore the degradation discrepancies between surgical and natural scenes from structural perception and semantic understanding perspectives, providing fundamental insights for domain-specific image restoration research. Our work aims to empower restoration algorithms and improve the efficiency of clinical procedures. Sources can be available at: https://github.com/PJLallen/Surgical-Image-Restoration.