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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PFMFUH
Repositorysid.inpe.br/sibgrapi/2017/08.21.00.34
Last Update2017:08.21.00.34.08 administrator
Metadatasid.inpe.br/sibgrapi/2017/08.21.00.34.08
Metadata Last Update2020:02.19.02.01.28 administrator
Citation KeyGoncalvesGayaDrewBote:2017:EnDeMe
TitleDeepDive: An End-to-End Dehazing Method Using Deep Learning
FormatOn-line
Year2017
DateOct. 17-20, 2017
Access Date2021, Jan. 19
Number of Files1
Size1083 KiB
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Author1 Goncalves, Lucas Teixeira
2 Gaya, Joel de Oliveira
3 Drews-Jr, Paulo
4 Botelho, Silvia Silva da Costa
Affiliation1 Universidade Federal do Rio Grande
2 Universidade Federal do Rio Grande
3 Universidade Federal do Rio Grande
4 Universidade Federal do Rio Grande
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addresslucasteixeirag11@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 00:34:08 :: lucasteixeirag11@gmail.com -> administrator ::
2020-02-19 02:01:28 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsDeep Learning, Image Dehazing, Convolutional Neural Network.
AbstractImage dehazing can be described as the problem of mapping from a hazy image to a haze-free image. Most approaches to this problem use physical models based on simplifications and priors. In this work we demonstrate that a convolutional neural network with a deep architecture and a large image database is able to learn the entire process of dehazing, without the need to adjust parameters, resulting in a much more generic method. We evaluate our approach applying it to real scenes corrupted by haze. The results show that even though our network is trained with simulated indoor images, it is capable of dehazing real outdoor scenes, learning to treat the degradation effect itself, not to reconstruct the scene behind it.
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PFMFUH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFMFUH
Languageen
Target FilePID4958913.pdf
User Grouplucasteixeirag11@gmail.com
Visibilityshown
Update Permissionnot transferred
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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