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Reference TypeConference Paper (Conference Proceedings)
Last Update2017:
Metadata Last Update2020: administrator
Citation KeyNacifFern:2017:MuSuPa
TitleMulti-Frame super-resolução a partir de uma única imagem por estereoscopia
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size851 KiB
Context area
Author1 Nacif, Felippe Leitão
2 Fernandes, Leandro Augusto Frata
Affiliation1 Universidade Federal Fluminense
2 Universidade Federal Fluminense
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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeUndergraduate Work
History2017-09-09 02:55:51 :: -> administrator ::
2020-02-20 22:06:48 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Keywordssuper-resolução, lentes estereoscópicas, iterative back projection.
AbstractSuper-resolution (SR) is a set of techniques that estimates a high-resolution (HR) image from one or more low-resolution (LR) images. Single-frame SR usually relies on learn-based methods to enhance a LR image. Multi-frame SR, on the other hand, may not assume previous knowledge of the scene and use information extracted from multiple LR images taken from slightly different viewpoints to estimate a HR view. We present a multi-frame SR approach that uses a stereoscopic camera lens attachment to capture two overlapped views of the scene at the same time with the same image sensor. We adapt the classic Iterative Back Projection (IBP) SR algorithm to estimate a SR color image from two color LR views. We virtually modeled the proposed lens attachment and demonstrate that, by using only two overlapped views, our approach achieves qualitatively better results than other multi-frame SR techniques.
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