Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PFMFUH |
Repository | sid.inpe.br/sibgrapi/2017/08.21.00.34 |
Last Update | 2017:08.21.00.34.08 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/08.21.00.34.08 |
Metadata Last Update | 2020:02.19.02.01.28 administrator |
Citation Key | GoncalvesGayaDrewBote:2017:EnDeMe |
Title | DeepDive: An End-to-End Dehazing Method Using Deep Learning  |
Format | On-line |
Year | 2017 |
Date | Oct. 17-20, 2017 |
Access Date | 2021, Jan. 19 |
Number of Files | 1 |
Size | 1083 KiB |
Context area | |
Author | 1 Goncalves, Lucas Teixeira 2 Gaya, Joel de Oliveira 3 Drews-Jr, Paulo 4 Botelho, Silvia Silva da Costa |
Affiliation | 1 Universidade Federal do Rio Grande 2 Universidade Federal do Rio Grande 3 Universidade Federal do Rio Grande 4 Universidade Federal do Rio Grande |
Editor | Torchelsen, 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 Address | lucasteixeirag11@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2017-08-21 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 Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Deep Learning, Image Dehazing, Convolutional Neural Network. |
Abstract | Image 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. |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PFMFUH |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PFMFUH |
Language | en |
Target File | PID4958913.pdf |
User Group | lucasteixeirag11@gmail.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PJT9LS 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
| |