Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정제창 | - |
dc.date.accessioned | 2016-12-01T05:16:43Z | - |
dc.date.available | 2016-12-01T05:16:43Z | - |
dc.date.issued | 2015-05 | - |
dc.identifier.citation | JOURNAL OF SENSORS, v. 2016, Page. 1-2 | en_US |
dc.identifier.issn | 1687-725X | - |
dc.identifier.issn | 1687-7268 | - |
dc.identifier.uri | https://www.hindawi.com/journals/js/2016/3894832/ | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/24637 | - |
dc.description.abstract | Nowadays many camera-based advanced driver assistance systems (ADAS) have been introduced to assist the drivers and ensure their safety under various driving conditions. One of the problems faced by drivers is the faded scene visibility and lower contrast while driving in foggy conditions. In this paper, we present a novel approach to provide a solution to this problem by employing deep neural networks. We assume that the fog in an image can be mathematically modeled by an unknown complex function and we utilize the deep neural network to approximate the corresponding mathematical model for the fog. The advantages of our technique are as follows: (i) its real-time operation and (ii) being based on minimal input, that is, a single image, and exhibiting robustness/generalization for various unseen image data. Experiments carried out on various synthetic images indicate that our proposed technique has the abilities to approximate the corresponding fog function reasonably and remove it for better visibility and safety. | en_US |
dc.description.sponsorship | This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Republic of Korea, under the project for Technical Development of Information Communication and Broadcasting, supervised by IITP (Institute of Information and Communications Technology Promotion) (IITP 2015-B0101-15-1377). | en_US |
dc.language.iso | en | en_US |
dc.publisher | HINDAWI PUBLISHING CORPORATION | en_US |
dc.subject | VISION | en_US |
dc.title | Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1155/2016/3894832 | - |
dc.relation.page | 1-2 | - |
dc.relation.journal | JOURNAL OF SENSORS | - |
dc.contributor.googleauthor | Hussain, Farhan | - |
dc.contributor.googleauthor | Jeong, Jechang | - |
dc.relation.code | 2015013489 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF ELECTRONIC ENGINEERING | - |
dc.identifier.pid | jjeong | - |
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