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 |
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.