Smoke generated by surgical instruments during laparoscopic surgery can
obscure the visual field, impairing surgeons' ability to perform operations
accurately and safely. Thus, smoke removal task for laparoscopic images is
highly desirable. Despite laparoscopic image desmoking has attracted the
attention of researchers in recent years and several algorithms have emerged,
the lack of publicly available high-quality benchmark datasets is the main
bottleneck to hamper the development progress of this task. To advance this
field, we construct a new high-quality dataset for Laparoscopic Surgery image
Desmoking, named LSD3K, consisting of 3,000 paired synthetic non-homogeneous
smoke images. In this paper, we provide a dataset generation pipeline, which
includes modeling smoke shape using Blender, collecting ground-truth images
from the Cholec80 dataset, random sampling of smoke masks and etc. Based on the
proposed benchmark, we further conducted a comprehensive evaluation of the
existing representative desmoking algorithms. The proposed dataset is publicly
available at
this https URL

通过构建名为 LSD3K 的新型高质量数据集，该数据集包括 3,000 个配对的合成非均匀烟雾图像，本文提供了用于腹腔镜手术图像去雾的数据集生成流程，并在该基准上对现有的代表性去烟雾算法进行了全面评估。

LSD3K：腹腔镜手术图像烟雾去除的评估基准

LSD3K: A Benchmark for Smoke Removal from Laparoscopic Surgery Images

This paper presents a novel method of smoke removal from the laparoscopic
images. Due to the heterogeneous nature of surgical smoke, a two-stage network
is proposed to estimate the smoke distribution and reconstruct a clear,
smoke-free surgical scene. The utilization of the lightness channel plays a
pivotal role in providing vital information pertaining to smoke density. The
reconstruction of smoke-free image is guided by a hybrid embedding, which
combines the estimated smoke mask with the initial image. Experimental results
demonstrate that the proposed method boasts a Peak Signal to Noise Ratio that
is $2.79\%$ higher than the state-of-the-art methods, while also exhibits a
remarkable $38.2\%$ reduction in run-time. Overall, the proposed method offers
comparable or even superior performance in terms of both smoke removal quality
and computational efficiency when compared to existing state-of-the-art
methods. This work will be publicly available on
this http URL

本文提出了一种新颖的方法，用于从腹腔镜图像中去除烟雾。该方法采用了二阶段网络来估计烟雾分布并重建清晰的无烟手术场景，光度通道的利用对提供烟雾密度的重要信息起着关键作用。实验结果表明，该方法在峰值信噪比方面比现有最先进的方法高出 2.79％，运行时间也减少了 38.2％。总体上，与现有最先进的方法相比，该方法在烟雾去除质量和计算效率方面具有可比甚至更优秀的性能。