@inproceedings{b9c2d2ebcfd544d09b533cfe2ab66ff1,
title = "Joint desmoking, specularity removal, and denoising of laparoscopy images via graphical models and Bayesian inference",
abstract = "Laparoscopic images exhibit artifacts resulting from surgical smoke, specular highlights, and noise. These artifacts degrade the results of subsequent processing (e.g., tracking, segmentation, and depth analysis) and compromise surgical quality. We formulate a unified Bayesian inference problem for desmoking, specularity removal, and denoising in laparoscopic images. We propose novel probabilistic graphical models and sparse dictionary models as image priors. For inference, we rely on variational Bayesian expectation maximization. Results on simulated and real-world laparoscopic images, including clinical expert evaluation, show that our joint optimization method outperforms the state of the art.",
keywords = "Denoising, Desmoking, EM, Graphical models, Laparoscopy, Specularity removal, Variational Bayes",
author = "Ayush Baid and Alankar Kotwal and Riddhish Bhalodia and Merchant, {S. N.} and Awate, {Suyash P.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 ; Conference date: 18-04-2017 Through 21-04-2017",
year = "2017",
month = jun,
day = "15",
doi = "10.1109/ISBI.2017.7950623",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "732--736",
booktitle = "2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017",
}