dc.contributor.author | Bolkar, Sabri | |
dc.contributor.author | Wang, Congcong | |
dc.contributor.author | Alaya Cheikh, Faouzi | |
dc.contributor.author | Yildirim Yayilgan, Sule | |
dc.date.accessioned | 2019-03-29T11:14:04Z | |
dc.date.available | 2019-03-29T11:14:04Z | |
dc.date.created | 2018-09-12T14:12:19Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Proceedings of IEEE international conference on image processing. 2018, 3403-3407. | nb_NO |
dc.identifier.issn | 1522-4880 | |
dc.identifier.uri | http://hdl.handle.net/11250/2592442 | |
dc.description.abstract | During video-guided minimally invasive surgery, quality of frames may be degraded severely by cauterization-induced smoke and condensation of vapor. This degradation of quality creates discomfort for the operating surgeon, and causes serious problems for automatic follow-up processes such as registration, segmentation and tracking. This paper proposes a novel deep neural network based smoke removal solution that is able to enhance the quality of surgery video frames in real-time. It employs synthetically generated training dataset including smoke embedded and clean reference versions. Results calculated on the test set indicate that our network outperforms previous defogging methods in terms of quantitative and qualitative measures. While eliminating apparent smoke, it also successfully preserves the natural appearance of tissue surface. To the best of our knowledge, the presented method is the first deep neural network based approach for the surgical field smoke removal problem. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | nb_NO |
dc.title | Deep Smoke Removal from Minimally Invasive Surgery Videos | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 3403-3407 | nb_NO |
dc.source.journal | Proceedings of IEEE international conference on image processing | nb_NO |
dc.identifier.doi | 10.1109/ICIP.2018.8451815 | |
dc.identifier.cristin | 1608910 | |
dc.relation.project | Norges forskningsråd: 247689 | nb_NO |
dc.description.localcode | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitcode | 194,63,30,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.unitname | Institutt for informasjonssikkerhet og kommunikasjonsteknologi | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |