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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RNPSS5
Repositorysid.inpe.br/sibgrapi/2018/08.31.18.41
Last Update2018:08.31.18.41.10 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/08.31.18.41.10
Metadata Last Update2022:06.14.00.09.17 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00017
Citation KeyGonçalvesGayaDrewBote:2018:SiImDe
TitleGuidedNet: Single Image Dehazing Using an End-to-end Convolutional Neural Network
FormatOn-line
Year2018
Access Date2024, May 03
Number of Files1
Size7086 KiB
2. Context
Author1 Gonçalves, Lucas Teixeira
2 Gaya, Joel Felipe de Oliveira
3 Drews-Jr, Paulo Jorge Lilles
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
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addresslucasteixeirag11@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-08-31 18:41:10 :: lucasteixeirag11@gmail.com -> administrator ::
2022-06-14 00:09:17 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsdeep learning
single image dehazing
convolutional neural networks
guided filter
AbstractPoor visibility is a common problem when capturing images in participating mediums such as mist or water. The problem of generating a haze-free image based on a hazy one can be described as image dehazing. Previous approaches dealt with this problem using physical models based on priors and simplifications. In this paper, we demonstrate that an end-to-end convolutional neural network is able to learn the dehazing process with no parameters or priors required, resulting in a more generic method. Even though our model is trained entirely with hazy indoor images, we are able to fully restore outdoor images with real haze. Also, we propose an architecture containing the novel Guided Layers, introduced in order to reduce the loss of spatial information while restoring the images. Our method outperforms other machine learning based models, yielding superior results both qualitatively and quantitatively.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > GuidedNet: Single Image...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > GuidedNet: Single Image...
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/3RNPSS5
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RNPSS5
Languageen
Target FileFINAL_FINAL_SIBIGRAPI.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/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 13
sid.inpe.br/banon/2001/03.30.15.38.24 1
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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