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		<doi>10.1109/SIBGRAPI.2015.47</doi>
		<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>
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		<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, BA, Brazil</conferencelocation>
		<date>26-29 Aug. 2015</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<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>
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		<usergroup>hsjunior@ifes.edu.br</usergroup>
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