Single image dehazing with bright object handling
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Riaz, Irfan | - |
dc.contributor.author | Fan, Xue | - |
dc.contributor.author | Shin, Hyunchul | - |
dc.date.accessioned | 2021-06-22T15:44:28Z | - |
dc.date.available | 2021-06-22T15:44:28Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2016-12 | - |
dc.identifier.issn | 1751-9632 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/12186 | - |
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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | INST ENGINEERING TECHNOLOGY-IET | - |
dc.title | Single image dehazing with bright object handling | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shin, Hyunchul | - |
dc.identifier.doi | 10.1049/iet-cvi.2015.0451 | - |
dc.identifier.scopusid | 2-s2.0-85017559795 | - |
dc.identifier.wosid | 000396098800006 | - |
dc.identifier.bibliographicCitation | IET COMPUTER VISION, v.10, no.8, pp.817 - 827 | - |
dc.relation.isPartOf | IET COMPUTER VISION | - |
dc.citation.title | IET COMPUTER VISION | - |
dc.citation.volume | 10 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 817 | - |
dc.citation.endPage | 827 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | ADAPTIVE DARK CHANNEL | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | VISION | - |
dc.subject.keywordAuthor | ADAPTIVE DARK CHANNEL | - |
dc.subject.keywordAuthor | HAZE REMOVAL | - |
dc.subject.keywordAuthor | ENHANCEMENT | - |
dc.subject.keywordAuthor | FRAMEWORK | - |
dc.subject.keywordAuthor | WEATHER | - |
dc.subject.keywordAuthor | VISION | - |
dc.identifier.url | https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-cvi.2015.0451 | - |
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