Modified total variation regularization using fuzzy complement for image denoising

Ahmed Ben Said, Sebti Foufou

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

Abstract

In this paper, we propose a denoising algorithm based on the Total Variation (TV) model. Specifically, we associate to the regularization term of the Rodin-Osher-Fatimi (ROF) functional a small weight whenever denoising is performed in edge and texture regions, which means less regularization and more details preservation. On the other hand, a large weight is associated if the region being filtered is smooth which means noise will be well suppressed. The weight computation is inspired from the fuzzy edge complement. Experiments on well-known images and comparison with state of the art denoising algorithms demonstrate that the proposed method not only presents good denoising performance but also is able to preserve edge information.

Original languageEnglish (US)
Title of host publication2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781509003570
DOIs
StatePublished - Nov 28 2016
Event2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015 - Auckland, New Zealand
Duration: Nov 23 2015Nov 24 2015

Publication series

NameInternational Conference Image and Vision Computing New Zealand
Volume2016-November
ISSN (Print)2151-2191
ISSN (Electronic)2151-2205

Other

Other2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015
CountryNew Zealand
CityAuckland
Period11/23/1511/24/15

Keywords

  • denoising
  • edge detector
  • fuzzy complement
  • total variation

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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