{"url":"/dataset/desmoke-lap-dataset","name":"DeSmoke-LAP dataset","full_name":null,"description_markdown":"The laparoscopic surgery dataset is associated with our International Journal of Computer Assisted Radiology and Surgery (IJCARS) publication titled “DeSmoke-LAP: Improved Unpaired Image-to-Image Translation for Desmoking in Laparoscopic Surgery”. The training model of the proposed method is available as an open source on Github. We propose DeSmoke-LAP, a new method for removing smoke from real robotic laparoscopic hysterectomy videos. The proposed method is based on the unpaired image-to-image cycle-consistent generative adversarial network in which two novel loss functions, namely, inter-channel discrepancies and dark channel prior.\r\n\r\nThe dataset contains frames and video clips from 10 robot-assisted laparoscopic hysterectomy procedure videos. The original videos were decomposed into frames at 1 fps. From each video, 300 hazy images and 300 clear images were manually selected by observing the electrocauterisation. A short video clip of 50 frames from each procedure was also selected that was utilised for testing. 5-fold cross-validation was performed for all methods under comparison. Quantitative evaluation was done using referenceless metrics and qualitative evaluation was performed through a survey filled out by end-users (surgeons).","description_withheld":null,"homepage":"https://www.ucl.ac.uk/interventional-surgical-sciences/weiss-open-research/weiss-open-data-server/desmoke-lap","introduced_date":"2022-03-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/desmoke-lap-improved-unpaired-image-to-image","title":"DeSmoke-LAP: improved unpaired image-to-image translation for desmoking in laparoscopic surgery","first_author":"Yirou Pan","url":null},"license":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Image Dehazing","url":"/task/image-dehazing","datasets_with_task":"/datasets/task/image-dehazing"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["DeSmoke-LAP dataset"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}