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Authors: Tarek A. Mahmoud and Stephen Marshall

Affiliation: University of Strathclyde, United Kingdom

ISBN: 978-972-8865-73-3

Keyword(s): Edge detection, image sharpening, morphological filter, threshold decomposition.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Enhancement and Restoration ; Image Filtering ; Image Formation and Preprocessing ; Image Quality ; Mathematical Morphology

Abstract: A new method is proposed to sharpen digital images. This sharpening method is based on edge detection and a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators, such as Prewitt operators, are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the performance of these detected edge deblurring filters is superior to that of the traditional sharpening filter family.

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Paper citation in several formats:
A. Mahmoud T.; Marshall S. and (2007). THRESHOLD DECOMPOSITION DRIVEN ADAPTIVE MORPHOLOGICAL FILTER FOR IMAGE SHARPENING.In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 978-972-8865-73-3, pages 40-45. DOI: 10.5220/0002048400400045

@conference{visapp07,
author={Tarek {A. Mahmoud} and Stephen Marshall},
title={THRESHOLD DECOMPOSITION DRIVEN ADAPTIVE MORPHOLOGICAL FILTER FOR IMAGE SHARPENING},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2007},
pages={40-45},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002048400400045},
isbn={978-972-8865-73-3},
}

TY - CONF

JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - THRESHOLD DECOMPOSITION DRIVEN ADAPTIVE MORPHOLOGICAL FILTER FOR IMAGE SHARPENING
SN - 978-972-8865-73-3
AU - A. Mahmoud, T.
AU - Marshall, S.
PY - 2007
SP - 40
EP - 45
DO - 10.5220/0002048400400045

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