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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538675069
DOIs
StatePublished - 12 Sep 2018
Event2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - San Carlos, Costa Rica
Duration: 18 Jul 201820 Jul 2018

Publication series

Name2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings

Conference

Conference2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018
Country/TerritoryCosta Rica
CitySan Carlos
Period18/07/1820/07/18

Keywords

  • Image Processing
  • Non Local Means filter
  • Optimization
  • Parallelization
  • Xeon Phi Knights Landing

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