%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2015/06.19.20.59
%2 sid.inpe.br/sibgrapi/2015/06.19.20.59.50
%@doi 10.1109/SIBGRAPI.2015.47
%T Fast and Effective Geometric K-Nearest Neighbors Multi-Frame Super-Resolution
%D 2015
%A Seibel Junior, Hilario,
%A Goldenstein, Siome,
%A Rocha, Anderson,
%@affiliation Instituto Federal do Espirito Santo, Universidade Estadual de Campinas
%@affiliation Universidade Estadual de Campinas
%@affiliation Universidade Estadual de Campinas
%E Papa, Joćo Paulo,
%E Sander, Pedro Vieira,
%E Marroquim, Ricardo Guerra,
%E Farrell, Ryan,
%B Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)
%C Salvador, BA, Brazil
%8 26-29 Aug. 2015
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K super-resolution, geometric k-NN, multi-frame, burst, mobile devices.
%X 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.
%@language en
%3 PID3771795.pdf