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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFMFUH
Repositorysid.inpe.br/sibgrapi/2017/08.21.00.34
Last Update2017:08.21.00.34.08 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.21.00.34.08
Metadata Last Update2022:06.14.00.08.52 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.64
Citation KeyGoncalvesGayaDrewBote:2017:EnDeMe
TitleDeepDive: An End-to-End Dehazing Method Using Deep Learning
FormatOn-line
Year2017
Access Date2024, May 02
Number of Files1
Size1083 KiB
2. Context
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, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-21 00:34:08 :: lucasteixeirag11@gmail.com -> administrator ::
2022-06-14 00:08:52 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > DeepDive: An End-to-End...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > DeepDive: An End-to-End...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFMFUH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFMFUH
Languageen
Target FilePID4958913.pdf
User Grouplucasteixeirag11@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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