Multispectral image denoising using optimized vector NLM filter

Ahmed Ben Said, Sebti Foufou

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we present a Stein’s Unbiased Risk Estimator (SURE) approach for Non-Local Mean filter to denoise multispectral images. We extend this filter to the vector case in order to take advantage from the additional spectral information brought by the multispectral imaging system. Experimental results show that the proposed optimized vector non-local mean filter (OVNLM) presented good denoising performance compared to several other approaches.

Original languageEnglish (US)
Title of host publicationImage and Video Technology - 7th Pacific-Rim Symposium, PSIVT 2015, Revised Selected Papers
EditorsMariano Rivera, Brendan McCane, Xinguo Yu, Thomas Bräunl
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319294506
StatePublished - 2016
Event7th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2015 - Auckland, New Zealand
Duration: Nov 25 2015Nov 27 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2015
Country/TerritoryNew Zealand


  • Multispectral image
  • Stein’s Unbiased Risk Estimator
  • Vector non-local mean filter

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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