Edge guided total variation for image denoising

Ahmed Ben Said, Rachid Hadjidj, Sebti Foufou, Mongi Abidi

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

Abstract

In this paper, we present a novel denoising algorithm based on the Rodin-Osher-Fatemi (ROF) model. The goal is to ensure maximum noise removal while preserving image details. To achieve this goal, we developed a new edge detector based on the structure tensor, Non-Local Mean filtering and fuzzy complement. This edge detector is incorporated in the objective function of the ROF model to introduce more control over the amount of regularization allowing more denoising in smooth regions and less denoising when processing edge regions. Experiments on synthetic images demonstrate the efficiency of the edge detector. Furthermore, denoising experiments and comparison with other algorithms show that the proposed method presents good performance in terms of Peak Signal-to-Noise Ratio and Structure Similarity Index.

Original languageEnglish (US)
Title of host publication2017 51st Annual Conference on Information Sciences and Systems, CISS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509047802
DOIs
StatePublished - May 10 2017
Event51st Annual Conference on Information Sciences and Systems, CISS 2017 - Baltimore, United States
Duration: Mar 22 2017Mar 24 2017

Publication series

Name2017 51st Annual Conference on Information Sciences and Systems, CISS 2017

Other

Other51st Annual Conference on Information Sciences and Systems, CISS 2017
CountryUnited States
CityBaltimore
Period3/22/173/24/17

Keywords

  • Edge detection
  • Image denoising
  • Total variation

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems and Management
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Said, A. B., Hadjidj, R., Foufou, S., & Abidi, M. (2017). Edge guided total variation for image denoising. In 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017 [7926122] (2017 51st Annual Conference on Information Sciences and Systems, CISS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2017.7926122