  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
https://drive.google.com/file/d/1v0U5_3S4nJpaUiP898Q0pc-MfEAtnbOq/view?usp=sharing
