Rough Sets-Based Image Processing for Deinterlacing

  • Gwanggil Jeon
  • Jechang Jeong
Part of the Multimedia Systems and Applications Series book series (MMSA, volume 31)


This chapter includes the rough sets theory for video deinterlacing that has been both researched and applied. The domain knowledge of several experts influences the decision making aspects of this theory. However, included here are a few studies that discuss the effectiveness of the rough sets concept in the field of engineering. Moreover, the studies involving a deinterlacing system that are based on rough sets have not been proposed yet. This chapter introduces a deinterlacing method that will reliably confirm that the method being tested is the most suitable for the sequence. This approach employs a reduced database system size, which contains the essential information for the process. Decision making and interpolation results are presented. The results of computer simulations show that the proposed method outperforms a number of methods that are presented in literature.


Decision Rule Decision Table Minimal Decision Output Image Quality Deinterlacing Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    T. Doyle, “Interlaced to sequential conversion for EDTV applications,” in Proc. 2nd Int. Workshop Signal Processing of HDTV, pp. 412-430 Feb. 1990Google Scholar
  2. 2.
    E. B. Bellers and G. de Haan, “Advanced de-interlacing techniques,” in Proc. ProRisc/IEEE Workshop on Circuits, Systems and Signal Processing, Mierlo, The Netherlands, Nov. 1996, pp. 7-17Google Scholar
  3. 3.
    P. L. Swan, “Method and apparatus for providing interlaced video on a progressive display,” U.S. Patent 5 864 369, Jan. 26, 1999 238 Gwanggil Jeon and Jechang JeongGoogle Scholar
  4. 4.
    H. -S. Oh, Y. Kim, Y. -Y. Jung, A.W. Morales, and S. -J. Ko, “Spatio-temporal edge-based median filtering for deinterlacing,” IEEE International Conference on Consumer Electronics, pp. 52-53, 2000Google Scholar
  5. 5.
    Z. Pawlak - “Rough Sets - Theoretical Aspects of Reasoning about Data,” Klumer Academic Publishers, 1991Google Scholar
  6. 6.
    Q. Wu, X. Huang, and S. Van, “Multi-knowledge for robot to identify environments,” in Proc. WCICA 2004, Hangzhou, China, 2004, pp. 4840 - 4845 Vol.6.Google Scholar
  7. 7.
    M. Li, X. -F. Zhang, “ Knowledge entropy in rough set theory,” in Proc. ICMLC 2005, Shanghai, China, pp. 1408 - 1412 vol.3Google Scholar
  8. 8.
    M. Liu, Y. He, H. Hu, and D. Yu, “Dimension reduction based on rough set in image mining,” in Proc. CIT 2004, Chennai, India, pp. 39 - 44Google Scholar
  9. 9.
    X. -F. Zhang, F. -Z. Zhang, and Y. -S. Zhao, “Generalization of RST in ordered information table,” in Proc. ICMLC 2005, Guangzhou, China, 2005, pp. 2027 - 2032 Vol. 4Google Scholar
  10. 10.
    L. Pan, H. Zheng, S. Nahavandi, “The application of rough set and Kohonen network to feature selection for object extraction,” in Proc. ICMLC 2003, Xi’an, China, 2003, pp. 1185 - 1189 Vol.2Google Scholar
  11. 11.
    J. W. Grzymala-Busse, “LERS - A system for learning from examples based on rough sets,” in R. Slowinski (Ed.) Intelligent Decision Support. Handbook of Applications an Advances of the Rough Set Theory, Kluwer Academic Publishers, Dordrecht, 1992Google Scholar
  12. 12.
    Xiaohua Hu, “Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications,” in Proc. ICDM 2001, San Jose, California, 2001, pp. 233 - 240.Google Scholar
  13. 13.
    A. Mohabey and A. K. Ray, “Rough set theory based segmentation of color images,” in Proc. NAFIPS 2000, Atlanta, GA, pp. 338 - 342Google Scholar
  14. 14.
    S. Mitatha, K. Dejharn, F. Chevasuvit, B. Chankuang, and W. Kasemsiri, “Experimental results of using rough sets for printed Thai characters recognition,” in Proc. TENCON 2001, Cairns, Thailand, pp. 331 - 334 vol.1Google Scholar
  15. 15.
    X. Wu and Q. Wang “Application of rough set attributes reduction in quality evaluation of dissertation,” in Proc. ICGC 2006, Atlanta, GA, pp. 562 - 565Google Scholar
  16. 16.
    Y. Peng, G. Liu, T. Lin, and H. Geng, “Application of rough set theory in network fault diagnosis,” in Proc. ICITA 2005, Hangzhou, China, 2005, 556 - 559 vol.2Google Scholar
  17. 17.
    Y. -H. Xu, W. -J. Jiang, and Y. -S. Xu, “Research on extracting medical diagnosis rules based on rough sets theory,” in Proc. ICMLC 2005, Guangzhou, China, 2005, pp. 3713 - 3718 Vol. 6.Google Scholar
  18. 18.
    S. Hirano, X. Sun, and S. Tsumoto, “Dealing with multiple types of expert knowledge in medical image segmentation: a rough sets style approach,” in Proc. FUZZ-IEEE’02, Honolulu, Hawaii, 2002, pp. 884 - 889Google Scholar
  19. 19.
    A. Kusiak, “Rough Set Theory: A data mining tool for semiconductor manufacturing,” IEEE Trans. Electronics Packaging Manufacturing, vol. 24, no. 1, pp. 44-50, Jan. 2001CrossRefGoogle Scholar
  20. 20.
    A. K. Agrawal and A. Agarwal, “Rough logic for building a landmine classifier,” in Proc. ICNSC 2005, Tucson, AZ, pp. 855 - 860Google Scholar
  21. 21.
    F. Su, C. Zhou, and W. Shi, “Geoevent association rule discovery model based on rough set with marine fishery application,” in Proc. IGARSS 2004, Anchorage, Alaska, 2004, pp. 1455 - 1458 vol.2. 20 Rough Sets-Based Image Processing for Deinterlacing 239Google Scholar
  22. 22.
    L. Torres, “Application of rough sets in power system control center data mining,” in Proc. PESW 2002, New York, NY, pp. 627 - 631 vol.1.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Gwanggil Jeon
  • Jechang Jeong

There are no affiliations available

Personalised recommendations