@inproceedings{9532c857fbb8467bbf075be3cf12fb36,
title = "Joint desmoking and denoising of laparoscopy images",
abstract = "Laparoscopic images in minimally invasive surgery get corrupted by surgical smoke and noise. This degrades the quality of the surgery and the results of subsequent processing for, say, segmentation and tracking. Algorithms for desmoking and denoising laparoscopic images seem to be missing in the medical vision literature. This paper formulates the problem of joint desmoking and denoising of laparoscopic images as a Bayesian inference problem. It relies on a novel probabilistic graphical model of the images, which includes novel prior models on the uncorrupted color image as well as the transmission-map image that indicates color attenuation due to smoke. The results on simulated and real-world laparoscopic images, including clinical expert evaluation, shows the advantages of the proposed method over the state of the art.",
keywords = "Laparoscopy, denoising, desmoking",
author = "Alankar Kotwal and Riddhish Bhalodia and Awate, {Suyash P.}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 ; Conference date: 13-04-2016 Through 16-04-2016",
year = "2016",
month = jun,
day = "15",
doi = "10.1109/ISBI.2016.7493446",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1050--1054",
booktitle = "2016 IEEE International Symposium on Biomedical Imaging",
}