DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

Saul Calderon, Jorge Castro, Manuel Zurnbado

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3 Citas (Scopus)

Resumen

This paper presents a novel computational optimization of the deceived non local means filter using moving average and symmetric weighting. The proposed optimization is compared with different approaches that reduce the computational cost of the deceived non local means filter. Furthermore, the impact of parallelizing different optimization approaches is assessed by evaluating the execution time and scalability in Xeon Phi KNL architecture. The proposed optimization for the sequential implementation achieved a 90x speedup, while its parallelized implementation yielded a speedup of up to 1662x.

Idioma originalInglés
Título de la publicación alojada2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión impresa)9781538675069
DOI
EstadoPublicada - 12 sept 2018
Evento2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - San Carlos, Costa Rica
Duración: 18 jul 201820 jul 2018

Serie de la publicación

Nombre2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings

Conferencia

Conferencia2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018
País/TerritorioCosta Rica
CiudadSan Carlos
Período18/07/1820/07/18

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