Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 신현철 | - |
dc.date.accessioned | 2018-05-14T06:24:40Z | - |
dc.date.available | 2018-05-14T06:24:40Z | - |
dc.date.issued | 2016-12 | - |
dc.identifier.citation | IET COMPUTER VISION, v. 10, No. 8, Page. 817-827 | en_US |
dc.identifier.issn | 1751-9632 | - |
dc.identifier.issn | 1751-9640 | - |
dc.identifier.uri | http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0451 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/71364 | - |
dc.description.abstract | This study addresses the shortcomings of the dark channel prior (DCP). The authors propose a new and efficient method for transmission estimation with bright-object handling capability. Based on the intensity value of a bright surface, they categorise DCP failures into two types: (i) obvious failure: occurs on surfaces that are brighter than ambient light. They show that, for these surfaces, altering the transmission value proportional to the brightness is better than the thresholding strategy; (ii) non-obvious failure: occurs on surfaces that are brighter than the neighbourhood average haziness value. Based on the observation that the transmission of a surface is loosely connected to its neighbours, the local average haziness value is used to recompute the transmission of such surfaces. This twofold strategy produces a better estimate of block and pixel-level haze thickness than DCP. To reduce haloes, a reliability map of block-level haze is generated. Then, via reliability-guided fusion of block-and pixel-level haze values, a high-quality refined transmission is obtained. Experimental results show that the authors' method competes well with state-of-the-art methods in typical benchmark images while outperforming these methods in more challenging scenarios. The authors' proposed reliability-guided fusion technique is about 60 times faster than other well-known DCP-based approaches. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MOE) (NRF-2013R1A1A2004421). Moreover, the author Irfan Riaz is sponsored by the Higher Education Commission (HEC) of the Government of Pakistan. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | INST ENGINEERING TECHNOLOGY-IET | en_US |
dc.subject | ADAPTIVE DARK CHANNEL | en_US |
dc.subject | HAZE REMOVAL | en_US |
dc.subject | ENHANCEMENT | en_US |
dc.subject | FRAMEWORK | en_US |
dc.subject | WEATHER | en_US |
dc.subject | VISION | en_US |
dc.title | Single image dehazing with bright object handling | en_US |
dc.type | Article | en_US |
dc.relation.no | 8 | - |
dc.relation.volume | 10 | - |
dc.identifier.doi | 10.1049/iet-cvi.2015.0451 | - |
dc.relation.page | 817-827 | - |
dc.relation.journal | IET COMPUTER VISION | - |
dc.contributor.googleauthor | Riaz, Irfan | - |
dc.contributor.googleauthor | Fan, Xue | - |
dc.contributor.googleauthor | Shin, Hyunchul | - |
dc.relation.code | 2016000250 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DIVISION OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | shin | - |
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