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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JMNT4P
Repositorysid.inpe.br/sibgrapi/2015/06.19.20.59
Last Update2015:06.19.20.59.50 hsjunior@ifes.edu.br
Metadatasid.inpe.br/sibgrapi/2015/06.19.20.59.50
Metadata Last Update2020:02.19.02.14.03 administrator
Citation KeySeibelJrGoldRoch:2015:FaEfGe
TitleFast and Effective Geometric K-Nearest Neighbors Multi-Frame Super-Resolution
FormatOn-line
Year2015
Access Date2021, Mar. 02
Number of Files1
Size564 KiB
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Author1 Seibel Junior, Hilario
2 Goldenstein, Siome
3 Rocha, Anderson
Affiliation1 Instituto Federal do Espirito Santo, Universidade Estadual de Campinas
2 Universidade Estadual de Campinas
3 Universidade Estadual de Campinas
EditorPapa, Joćo Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addresshsjunior@ifes.edu.br
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2015-06-19 20:59:50 :: hsjunior@ifes.edu.br -> administrator ::
2020-02-19 02:14:03 :: administrator -> :: 2015
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordssuper-resolution, geometric k-NN, multi-frame, burst, mobile devices.
AbstractMulti-frame super-resolution is possible when there is motion and non-redundant information from a sequence of low-resolution input images. Remote sensors, surveillance videos and modern mobile phones are examples of devices able to easily gather multiple images of a same scene. However, combining a large number of frames into a higher resolution image may not be computationally feasible by complex super-resolution techniques. We discuss herein a set of simple and effective high-performance algorithms to fastly super-resolve several low-resolution images in an always-on low-power environment, with possible applications in mobile computing, forensics, and biometrics. The algorithms rely on geometric k-nearest neighbors to decide which information to consider in each high-resolution pixel, have a low memory footprint and run in linear time as we increase the number of low-resolution input images. Finally, we suggest a minimum number of input images for multi-frame super-resolution, considering that we expect a good response as fast as possible.
ArrangementSIBGRAPI 2015 > Fast and Effective...
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data URLhttp://urlib.net/rep/8JMKD3MGPBW34M/3JMNT4P
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JMNT4P
Languageen
Target FilePID3771795.pdf
User Grouphsjunior@ifes.edu.br
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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