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Application of Fuzzy Logic and Lukasiewicz Operators for Image Contrast Control

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New Advances in Intelligent Signal Processing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 372))

Abstract

This chapter reviews image enhancement techniques. In particular the chapter is focused in soft computing technique to improve the contrast of images. There is a wide variety of contrast control techniques. However, most are not suitable for hardware implementation. A technique to control the contrast in images based on the application of Lukasiewicz algebra operators and fuzzy logic is described. In particular, the technique is based on the bounded-sum and the bounded-product . The selection of the control parameters is performed by a fuzzy system. An interesting feature when applying these operators is that it allows low cost hardware realizations (in terms of resources) and high processing speed.

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References

  1. Chen, Z.Y., Abidi, B.R., Page, D.L., Abidi, M.A.: Gray-Level Grouping (GLG): An Automatic Method for Optimized Image Contrast Enhancement-Part I: The Basic Method. IEEE Transactions on Image Processing 15(8), 2290–2302 (2006)

    Article  Google Scholar 

  2. Khellaf, A., Beghdadi, A., Dupoisot, H.: Entropic Contrast Enhancement. IEEE Transactions on Medical Imaging 10(4), 589–592 (1991)

    Article  Google Scholar 

  3. Gonzalez, R.C., Wintz, P.: Digital Image Processing. Addison-Wesley, Reading (1987)

    Google Scholar 

  4. Kim, S.Y., Han, D., Choi, S.J., Park, J.S.: Image Contrast Enhancement Based on the Piece-wise-Linear Approximation of CDF. IEEE Transactions on Consumer Electronics 45(3), 828–834 (1999)

    Article  Google Scholar 

  5. Mantiuk, R., Daly, S., Kerofsky, L.: Display Adaptive Tone Mapping. ACM Transactions on Graphics 27(3), 68-1–68-10 (2008)

    Google Scholar 

  6. Tizhoosh, H.R.: Fuzzy image enhancement: an overview. In: Kerre, E.E., Nachtegael, M. (eds.) Fuzzy Techniques in Image Processing. Springer, Heidelberg (2000)

    Google Scholar 

  7. Hauβecker, H., Tizhoosh, H.R.: Fuzzy Image Processing. In: Handbook of Computer Vision and Applications. Academic Press, London (1999)

    Google Scholar 

  8. Tizhoosh, H.R., Krell, G., Michaelis, B.: Enhancement: Contrast Adaptation Based on Optimization of Image Fuzziness. In: Proceedings of IEEE International Conference on Fuzzy Systems FUZZ-IEEE 1998, pp. 1548–1553 (1998)

    Google Scholar 

  9. Tizhoosh, H.R.: Adaptive -Enhancement: Type I versus Type II Fuzzy Implementation. In: IEEE Symp. Series on Computational Intelligence (2009)

    Google Scholar 

  10. Li, H., Yang, H.S.: Fast and Reliable Image Enhancement Using Fuzzy Relaxation Technique. IEEE Transactions on Systems, Man and Cybernetics 19(5), 1276–1281 (1989)

    Article  Google Scholar 

  11. Zhou, S.M., Gan, Q.: A New Fuzzy Relaxation Algorithm for Image Contrast Enhancement. In: International Symposium on Image and Signal Processing and Analysis, pp. 11–16 (2003)

    Google Scholar 

  12. Wirth, M.A., Nikitenko, D.: Applications of Fuzzy Morphology to Contrast Enhancement. In: Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2005, pp. 355–360 (2005)

    Google Scholar 

  13. Liu, G.J., Huang, J.H., Tang, X.L., Liu, J.F.: A Novel Fuzzy Wavelet Approach to Contrast Enhancement. In: International Conference on Machine Learning and Cybernetics, pp. 4325–4330 (2004)

    Google Scholar 

  14. Pal, S.K., King, R.A.: Image enhancement using fuzzy set. Electronic Letters 16(10), 376–378 (1980)

    Article  Google Scholar 

  15. Dong-liang, P., An-ke, X.: Degraded image enhancement with applications in robot vision. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1837–1842 (2005)

    Google Scholar 

  16. Hanmandlu, M., Jha, D., Sharma, R.: Color image enhancement by fuzzy intensification. In: International Conference on Pattern Recognition, vol. 3, pp. 310–313 (2000)

    Google Scholar 

  17. Hanmandlu, M., Jha, D.: An Optimal Fuzzy System for Color Image Enhancement. IEEE Transactions on Image Processing 15(10), 2956–2966 (2006)

    Article  Google Scholar 

  18. Hanmandlu, M., Verma, O.P., Kumar, N.K., Kulkarni, M.: A Novel Optimal Fuzzy System for Color Image Enhancement Using Bacterial Foraging. IEEE Transactions on Instrumentation and Measurement 58(8), 2867–2879 (2009)

    Article  Google Scholar 

  19. Vlachos, I.K., Sergiadis, G.D.: Intuistic Fuzzy Image Processing. In: Nachtegael, M., Van der Weken, D., Kerre, E.E., Philips, W. (eds.) Soft Computing in Image Processing. Springer, Heidelberg (2007)

    Google Scholar 

  20. Palaniappan, N., Srinivasan, R.: Applications of intuitionistic fuzzy sets of root type in image processing. In: North American Fuzzy Information Society Annual Conference, NAFIPS (2009)

    Google Scholar 

  21. Cheng, H.D., Xu, H.J.: Fuzzy approach to contrast enhancement. In: International Conference on Pattern Recognition, vol. 2, pp. 1549–1551 (1998)

    Google Scholar 

  22. Tizhoosh, H.R.: Fuzzy image processing. Springer, Heidelberg (1997) (in German)

    Google Scholar 

  23. Tizhoosh, H.R., Krell, G., Lilienblum, T., Moore, C.J., Michaelis, B.: Enhancement: and associative restoration of electronic portal images in radiotherapy. International Journal of Medical Informatics 49(2), 157–171 (1998)

    Article  Google Scholar 

  24. Russo, F.: An image enhancement technique combining sharpening and noise reduction. IEEE Transactions on Instrumentation and Measurement 51(4), 824–828 (2002)

    Article  Google Scholar 

  25. Kim, H.C., Kwon, B.H., Choi, M.R.: An Image Interpolator with Image Improvement for LCD Controller. IEEE Transactions on Consumer Electronics 47(2), 263–271 (2001)

    Article  Google Scholar 

  26. Cho, H.H., Choi, C.H., Kwon, B.H., Choi, M.R.: A Design of Contrast Controller for Image Improvement of Multi-Gray Scale Image. In: IEEE Asia Pacific Conference on ASICs, pp. 131–133 (2000)

    Google Scholar 

  27. Hussein, N.M., Barriga, A.: Image Contrast Control based on?ukasiewicz’s Operators. In: IEEE International Symposium on Intelligent Signal Processing (WISP 2009), pp. 131–135 (2009)

    Google Scholar 

  28. Hussein, N.M., Barriga, A.: Image Contrast Control based on?ukasiewicz’s Operators and Fuzzy Logic. In: International Conference on Intelligent Systems Design and Applications, ISDA 2009 (2009)

    Google Scholar 

  29. Sánchez-Solano, S., Barriga, A., Jiménez, C.J., Huertas, J.L.: Design and Applications of Digital Fuzzy Controllers. In: Proceedings of IEEE International Conference on Fuzzy Systems FUZZ-IEEE 1997, pp. 869–874 (1997)

    Google Scholar 

  30. Baturone, I., Barriga, A., Sánchez-Solano, S., Jiménez, C.J., López, D.: Microelectronic Design of Fuzzy Logic-Based Systems. CRC Press, Boca Raton (2000)

    Google Scholar 

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Authors and Affiliations

  1. Instituto de Microelectronica de Sevilla (CNM-CSIC), University of Seville, Spain

    Angel Barriga & Nashaat Mohamed Hussein Hassan

Authors
  1. Angel Barriga
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  2. Nashaat Mohamed Hussein Hassan
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Editor information

Editors and Affiliations

  1. CSI / Centre for Intelligent Systems, Universidade do Algarve, Campus de Gambelas, 8000, Faro, Portugal

    António E. Ruano

  2. Dept.of Mechatronics andVehicle Engineering, Óbuda University, Népszínház u. 8, 1081, Budapest, Hungary

    Annamária R. Várkonyi-Kóczy

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Barriga, A., Hassan, N.M.H. (2011). Application of Fuzzy Logic and Lukasiewicz Operators for Image Contrast Control. In: Ruano, A.E., Várkonyi-Kóczy, A.R. (eds) New Advances in Intelligent Signal Processing. Studies in Computational Intelligence, vol 372. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11739-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-11739-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11738-1

  • Online ISBN: 978-3-642-11739-8

  • eBook Packages: EngineeringEngineering (R0)

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