Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/41297
Título: Efficient parallelization of Motion Estimation for Super-Resolution
Autores/as: Marenzi, Elisa
Carrus, Andrea
Danese, Giovanni
Leporati, Francesco
Callico, Gustavo Marrero 
Clasificación UNESCO: 330790 Microelectrónica
Palabras clave: GPU
High Performance Computing
Super-Resolution
Fecha de publicación: 2017
Publicación seriada: Proceedings - 2017 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2017
Conferencia: 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) 
25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2017 
Resumen: This paper presents an efficient parallelization of the Motion Estimation procedure, one of the core parts of Super Resolution techniques. The algorithm considered is the basic version of Block Matching Super Resolution, with a single low-resolution camera and fixed Macro Block dimensions. Two are the implementations provided, with OpenMP and in CUDA on an NVIDIA Kepler GPU. Tests have been conducted on five image sequences and the results show a considerable improvement of the CUDA solution in all cases. Consequently, it can be stated that GPUs can efficiently accelerate computational times assuring the same image quality.
URI: http://hdl.handle.net/10553/41297
ISBN: 9781509060580
ISSN: 1066-6192
DOI: 10.1109/PDP.2017.64
Fuente: Proceedings - 2017 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2017 (7912659), p. 274-277
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