Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/41297
Title: Efficient parallelization of Motion Estimation for Super-Resolution
Authors: Marenzi, Elisa
Carrus, Andrea
Danese, Giovanni
Leporati, Francesco
Callico, Gustavo Marrero 
UNESCO Clasification: 330790 Microelectrónica
Keywords: GPU
High Performance Computing
Super-Resolution
Issue Date: 2017
Journal: Proceedings - 2017 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2017
Conference: 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 
Abstract: 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
Source: Proceedings - 2017 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2017 (7912659), p. 274-277
Appears in Collections:Actas de congresos
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