Skip to main content
SpringerLink
Log in

Alpha-Rooting Image Enhancement Using a Traditional Algorithm and Genetic Algorithm

  • Conference paper
Advance Trends in Soft Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 312))

Abstract

The application of soft computing in image/signal enhancement and comparing it with traditional methods will be discussed in this paper. This study presents two optimization methods for α-rooting image enhancement, which is a transform based method. The first method is a derivative-based optimization and the second one is Genetic Algorithm optimization. The parameter will be driven through optimization of measure of enhancement function (EME). The results from, the simulations show both methods are reliable; however, the first method has more computing cost.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zadeh, L.A.: Fuzzy Logic, Neural Networks, and Soft Computing. Communication of the ACM 37(3), 77–84 (1994)

    Article  MathSciNet  Google Scholar 

  2. Maini, R., Aggarwal, H.: A Comprehensive Review of Image Enhancement Techniques. Journal of Computing 2(3) (2010)

    Google Scholar 

  3. Genetic Algorithm, http://en.wikipedia.org/wiki/Genetic_algorithm (July 7, 2013 ) (retrieved )

  4. Agaian, S.S., Panetta, K., Grigoryan, A.M.: Transform-Based Image Enhancement Algorithms with Performance Measure. IEEE Transactions on Image Processing 10(3), 367–382 (2001)

    Article  MATH  Google Scholar 

  5. Arslan, F.T., Grigoryan, A.: Fast splitting alpha-rooting method of image enhancement: tensor representation 15(11), 3375–3384 (2006)

    Google Scholar 

  6. Paulinas, M., Ušinskas, A.: A Survey of Genetic Algorithms Applications for Image Enhancement and Segmentation. Information Technology and Control 36(3) (2007)

    Google Scholar 

  7. Genetic Algorithm (2013), http://www.mathworks.com/discovery/genetic-algorithm.html (retrieved )

  8. Rafael, C., Gonzalez, R.E.: Digital Image Processing, 3rd edn., Upper Saddle River, NJ (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX, 78249, USA

    Maryam Ezell, Azima Motaghi & Mo Jamshidi

Authors
  1. Maryam Ezell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Azima Motaghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mo Jamshidi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maryam Ezell .

Editor information

Editors and Affiliations

  1. The University of Texas, San Antonio, Texas, USA

    Mo Jamshidi

  2. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Ezell, M., Motaghi, A., Jamshidi, M. (2014). Alpha-Rooting Image Enhancement Using a Traditional Algorithm and Genetic Algorithm. In: Jamshidi, M., Kreinovich, V., Kacprzyk, J. (eds) Advance Trends in Soft Computing. Studies in Fuzziness and Soft Computing, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-03674-8_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-03674-8_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03673-1

  • Online ISBN: 978-3-319-03674-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation