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

    Other

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

    Fingerprint

    Image denoising
    Detectors
    Tensors
    Signal to noise ratio
    Experiments
    Processing
    Experiment
    Objective function
    Regularization

    Keywords

    • Edge detection
    • Image denoising
    • Total variation

    ASJC Scopus subject areas

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

    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] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2017.7926122

    Edge guided total variation for image denoising. / Said, Ahmed Ben; Hadjidj, Rachid; Foufou, Sebti; Abidi, Mongi.

    2017 51st Annual Conference on Information Sciences and Systems, CISS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7926122.

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

    Said, AB, 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, Institute of Electrical and Electronics Engineers Inc., 51st Annual Conference on Information Sciences and Systems, CISS 2017, Baltimore, United States, 3/22/17. https://doi.org/10.1109/CISS.2017.7926122
    Said AB, Hadjidj R, Foufou S, Abidi M. Edge guided total variation for image denoising. In 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 7926122 https://doi.org/10.1109/CISS.2017.7926122
    Said, Ahmed Ben ; Hadjidj, Rachid ; Foufou, Sebti ; Abidi, Mongi. / Edge guided total variation for image denoising. 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017. Institute of Electrical and Electronics Engineers Inc., 2017.
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