LSD3K: A Benchmark for Smoke Removal from Laparoscopic Surgery Images
This addresses the lack of public datasets for laparoscopic image desmoking, which hampers progress in improving surgical visibility, but it is incremental as it focuses on dataset creation rather than a new method.
The authors tackled the problem of smoke obscuring laparoscopic surgery images by constructing LSD3K, a new benchmark dataset of 3,000 paired synthetic smoke images, and used it to evaluate existing desmoking algorithms.
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