<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>sibgrapi.sid.inpe.br 802</site>
		<identifier>8JMKD3MGPBW34M/3JMNT4P</identifier>
		<repository>sid.inpe.br/sibgrapi/2015/06.19.20.59</repository>
		<lastupdate>2015:06.19.20.59.50 sid.inpe.br/banon/2001/03.30.15.38 hsjunior@ifes.edu.br</lastupdate>
		<metadatarepository>sid.inpe.br/sibgrapi/2015/06.19.20.59.50</metadatarepository>
		<metadatalastupdate>2020:02.19.02.14.03 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2015}</metadatalastupdate>
		<citationkey>SeibelJrGoldRoch:2015:FaEfGe</citationkey>
		<title>Fast and Effective Geometric K-Nearest Neighbors Multi-Frame Super-Resolution</title>
		<format>On-line</format>
		<year>2015</year>
		<numberoffiles>1</numberoffiles>
		<size>564 KiB</size>
		<author>Seibel Junior, Hilario,</author>
		<author>Goldenstein, Siome,</author>
		<author>Rocha, Anderson,</author>
		<affiliation>Instituto Federal do Espirito Santo, Universidade Estadual de Campinas</affiliation>
		<affiliation>Universidade Estadual de Campinas</affiliation>
		<affiliation>Universidade Estadual de Campinas</affiliation>
		<editor>Papa, Joćo Paulo,</editor>
		<editor>Sander, Pedro Vieira,</editor>
		<editor>Marroquim, Ricardo Guerra,</editor>
		<editor>Farrell, Ryan,</editor>
		<e-mailaddress>hsjunior@ifes.edu.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador</conferencelocation>
		<date>Aug. 26-29, 2015</date>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>super-resolution, geometric k-NN, multi-frame, burst, mobile devices.</keywords>
		<abstract>Multi-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.</abstract>
		<language>en</language>
		<targetfile>PID3771795.pdf</targetfile>
		<usergroup>hsjunior@ifes.edu.br</usergroup>
		<visibility>shown</visibility>
		<documentstage>not transferred</documentstage>
		<mirrorrepository>sid.inpe.br/banon/2001/03.30.15.38.24</mirrorrepository>
		<nexthigherunit>8JMKD3MGPBW34M/3K24PF8</nexthigherunit>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2015/06.19.20.59</url>
	</metadata>
</metadatalist>