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Authors: Hakaru Tamukoh 1 ; Noriaki Suetake 2 ; Hideaki Kawano 1 ; Ryosuke Kubota 3 ; Byungki Cha 4 and Takashi Aso 4

Affiliations: 1 Kyushu Institute of Technology, Japan ; 2 Yamaguchi University, Japan ; 3 Ube National College of Technology, Japan ; 4 Kyushu Institute of Information Sciences, Japan

Keyword(s): Image Enlargement, Image Reduction, Data Embedding, Fuzzy Inference.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Enhancement and Restoration ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors ; Image Generation Pipeline: Algorithms and Techniques

Abstract: This paper proposes a fuzzy-rule-embedded reduction image construction method for image enlargement. A fuzzy rule is generated by considering distribution of pixel value around a target pixel. The generated rule is embedded into the target pixel in a reduction image. The embedded fuzzy rule is used in a fuzzy inference to generate a highly magnified image from the reduction image. Experimental results, which scale factors are three and four, show that the proposed method realizes high-quality image enlargement in terms of both objective and subjective evaluations in comparison with conventional methods.

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Paper citation in several formats:
Tamukoh, H.; Suetake, N.; Kawano, H.; Kubota, R.; Cha, B. and Aso, T. (2014). Fuzzy-rule-embedded Reduction Image Construction Method for Image Enlargement with High Magnification. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP; ISBN 978-989-758-003-1; ISSN 2184-4321, SciTePress, pages 228-233. DOI: 10.5220/0004851802280233

@conference{visapp14,
author={Hakaru Tamukoh. and Noriaki Suetake. and Hideaki Kawano. and Ryosuke Kubota. and Byungki Cha. and Takashi Aso.},
title={Fuzzy-rule-embedded Reduction Image Construction Method for Image Enlargement with High Magnification},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP},
year={2014},
pages={228-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004851802280233},
isbn={978-989-758-003-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP
TI - Fuzzy-rule-embedded Reduction Image Construction Method for Image Enlargement with High Magnification
SN - 978-989-758-003-1
IS - 2184-4321
AU - Tamukoh, H.
AU - Suetake, N.
AU - Kawano, H.
AU - Kubota, R.
AU - Cha, B.
AU - Aso, T.
PY - 2014
SP - 228
EP - 233
DO - 10.5220/0004851802280233
PB - SciTePress

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