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Authors: M. El Hassouni and H. Cherifi

Affiliation: LIRSA, University of Dijon, France

Keyword(s): image restoration, lp-norm filter, α-stable processes, Fractional lower order statistics.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Enhancement and Restoration ; Image and Video Analysis ; Image Filtering ; Image Formation and Preprocessing ; Statistical Approach

Abstract: In this paper, we propose to use a class of filters based on fractional lower order statistics (FLOS) for still image restoration in the presence of α-stable noise. For this purpose, we present a family of 2-D finite-impulse response (FIR) adaptive filters optimized by the least mean lp-norm (LMP) algorithm. Experiments performed on natural images prove that the proposed algorithms provide superior performance in impulsive noise environments compared to LMS and Weighted Myriad filters.

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Paper citation in several formats:
El Hassouni, M. and Cherifi, H. (2006). A STATISTICAL BASED APPROACH FOR REMOVING HEAVY TAIL NOISE FROM IMAGES. In Proceedings of the First International Conference on Computer Vision Theory and Applications (VISIGRAPP 2006) - Volume 1: VISAPP; ISBN 972-8865-40-6; ISSN 2184-4321, SciTePress, pages 157-161. DOI: 10.5220/0001371901570161

@conference{visapp06,
author={M. {El Hassouni}. and H. Cherifi.},
title={A STATISTICAL BASED APPROACH FOR REMOVING HEAVY TAIL NOISE FROM IMAGES},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications (VISIGRAPP 2006) - Volume 1: VISAPP},
year={2006},
pages={157-161},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001371901570161},
isbn={972-8865-40-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the First International Conference on Computer Vision Theory and Applications (VISIGRAPP 2006) - Volume 1: VISAPP
TI - A STATISTICAL BASED APPROACH FOR REMOVING HEAVY TAIL NOISE FROM IMAGES
SN - 972-8865-40-6
IS - 2184-4321
AU - El Hassouni, M.
AU - Cherifi, H.
PY - 2006
SP - 157
EP - 161
DO - 10.5220/0001371901570161
PB - SciTePress

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