Luo Xiongbiao, Yang Fan, Zeng Hui-Qing, Du Yan-Ping
School of Informatics, Xiamen University, Xiamen 361005, People's Republic of China.
Zhongshan Hospital, Xiamen University, Xiamen 361005, People's Republic of China.
Healthc Technol Lett. 2019 Dec 6;6(6):280-285. doi: 10.1049/htl.2019.0095. eCollection 2019 Dec.
Endoscopic video sequences provide surgeons with direct surgical field or visualisation on anatomical targets in the patient during robotic surgery. Unfortunately, these video images are unavoidably hazy or foggy to prevent surgeons from clear surgical vision due to typical surgical operations such as ablation and cauterisation during surgery. This Letter aims at removing fog or smoke on endoscopic video sequences to enhance and maintain a direct and clear visualisation of the operating field during robotic surgery. The authors propose a new luminance blending framework that integrates contrast enhancement with visibility restoration for foggy endoscopic video processing. The proposed method was validated on clinical endoscopic videos that were collected from robotic surgery. The experimental results demonstrate that their method provides a promising means to effectively remove fog or smoke on endoscopic video images. In particular, the visual quality of defogged endoscopic images was improved from 0.5088 to 0.6475.
内窥镜视频序列为外科医生在机器人手术过程中提供了患者手术区域的直接视野或解剖目标的可视化。不幸的是，由于手术过程中的典型手术操作，如消融和烧灼，这些视频图像不可避免地会模糊或有雾，从而妨碍外科医生获得清晰的手术视野。本文旨在去除内窥镜视频序列中的雾或烟，以增强并保持机器人手术过程中手术区域的直接清晰可视化。作者提出了一种新的亮度融合框架，该框架将对比度增强与可见性恢复相结合，用于有雾内窥镜视频处理。所提出的方法在从机器人手术中收集的临床内窥镜视频上得到了验证。实验结果表明，他们的方法为有效去除内窥镜视频图像上的雾或烟提供了一种很有前景的手段。特别是，去雾后的内窥镜图像的视觉质量从0.5088提高到了0.6475。
