# Fuzzy-rule-embedded Reduction Image Construction Method for Image Enlargement with High Magnification

### Hakaru Tamukoh, Noriaki Suetake, Hideaki Kawano, Ryosuke Kubota, Byungki Cha, Takashi Aso

#### 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.

#### References

- Farsiu, S., Robinson, M., Elad, M., and Milanfar, P. (2004). Fast and robust multiframe super resolution. In IEEE Trans. Image Process., volume 13, pages 1327-1344.
- Greenspan, H., Anderson, C. H., and Akber, S. (2000). Image enhancement by nonlinear extrapolation in frequency space. In IEEE Trans. Image Process., volume 9, pages 1035-1048.
- Keys, R. G. (1981). Cubic convolution interpolation for digital image processing. In IEEE Trans. Acoust. Speech Signal Process, volume 26, pages 1153-1160.
- Lin, J. S. (1990). Two-dimensional signal processing and image processing. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1st edition.
- Siu, W. C. and Hung, K. W. (2012). Review of image interpolation and super-resolution. In Proc. of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pages 1-10.
- Sugeno, M. (1985). Industrial applications of fuzzy control. Elsevier Science Pub. Co.
- Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. In IEEE Trans. Systems, Man and Cybernetics, volume 15, pages 116-132.
- Tamukoh, H., Kawano, H., Suetake, N., Sekine, M., Cha, B., and Aso, T. (2013). A data embedded reduction image generation method for high-quality image enlargement. In Proc. of 7th Int. Conf. on Circuits, Systems, Signal and Telecommunications, pages 37-42.

#### Paper Citation

#### in Harvard Style

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 - Volume 1: VISAPP, (VISIGRAPP 2014)* ISBN 978-989-758-003-1, pages 228-233. DOI: 10.5220/0004851802280233

#### in Bibtex Style

@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 - Volume 1: VISAPP, (VISIGRAPP 2014)},

year={2014},

pages={228-233},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0004851802280233},

isbn={978-989-758-003-1},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)

TI - Fuzzy-rule-embedded Reduction Image Construction Method for Image Enlargement with High Magnification

SN - 978-989-758-003-1

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