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Reference TypeConference Proceedings
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3RNPSS5
Repositorysid.inpe.br/sibgrapi/2018/08.31.18.41
Last Update2018:08.31.18.41.10 administrator
Metadatasid.inpe.br/sibgrapi/2018/08.31.18.41.10
Metadata Last Update2020:02.19.03.10.44 administrator
Citation KeyGonçalvesGayaDrewBote:2018:SiImDe
TitleGuidedNet: Single Image Dehazing Using an End-to-end Convolutional Neural Network
FormatOn-line
Year2018
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size7086 KiB
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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
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-08-31 18:41:10 :: lucasteixeirag11@gmail.com -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
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Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Transferable1
Content TypeExternal Contribution
Tertiary TypeFull Paper
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.
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Target FileFINAL_FINAL_SIBIGRAPI.pdf
User Grouplucasteixeirag11@gmail.com
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Next Higher Units8JMKD3MGPAW/3RPADUS
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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