Pixel-Wise Wasserstein Autoencoder for Highly Generative Dehazing
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Guisik | - |
dc.contributor.author | Park, Sung Woo | - |
dc.contributor.author | Kwon, Junseok | - |
dc.date.accessioned | 2021-07-22T06:54:10Z | - |
dc.date.available | 2021-07-22T06:54:10Z | - |
dc.date.issued | 2021-06 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.issn | 1941-0042 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47752 | - |
dc.description.abstract | We propose a highly generative dehazing method based on pixel-wise Wasserstein autoencoders. In contrast to existing dehazing methods based on generative adversarial networks, our method can produce a variety of dehazed images with different styles. It significantly improves the dehazing accuracy via pixel-wise matching from hazy to dehazed images through 2-dimensional latent tensors of the Wasserstein autoencoder. In addition, we present an advanced feature fusion technique to deliver rich information to the latent space. For style transfer, we introduce a mapping function that transforms existing latent spaces to new ones. Thus, our method can produce highly generative haze-free images with various tones, illuminations, and moods, which induces several interesting applications, including low-light enhancement, daytime dehazing, nighttime dehazing, and underwater image enhancement. Experimental results demonstrate that our method quantitatively outperforms existing state-of-the-art methods for synthetic and real-world datasets, and simultaneously generates highly generative haze-free images, which are qualitatively diverse. | - |
dc.format.extent | 11 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Pixel-Wise Wasserstein Autoencoder for Highly Generative Dehazing | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TIP.2021.3084743 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON IMAGE PROCESSING, v.30, pp 5452 - 5462 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000659548200005 | - |
dc.identifier.scopusid | 2-s2.0-85107356887 | - |
dc.citation.endPage | 5462 | - |
dc.citation.startPage | 5452 | - |
dc.citation.title | IEEE TRANSACTIONS ON IMAGE PROCESSING | - |
dc.citation.volume | 30 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Tensors | - |
dc.subject.keywordAuthor | Image enhancement | - |
dc.subject.keywordAuthor | Lighting | - |
dc.subject.keywordAuthor | Network architecture | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Channel estimation | - |
dc.subject.keywordAuthor | Transforms | - |
dc.subject.keywordAuthor | Dehazing | - |
dc.subject.keywordAuthor | wasserstein autoencoder | - |
dc.subject.keywordAuthor | image enhancement | - |
dc.subject.keywordPlus | IMAGE | - |
dc.subject.keywordPlus | ENHANCEMENT | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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