Skip to main content
SpringerLink
Log in
Book cover

Nature Inspired Optimization Techniques for Image Processing Applications pp 189–213Cite as

Cuckoo Optimization Algorithm (COA) for Image Processing

  • Chapter
  • First Online:

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 150))

Abstract

Image optimization is the process of enhancing the image quality and visual appearance in order to provide preferable transfer representation for many future automated images such as medical images, satellite and aerial images which might suffer from poor and bad contrast and noise. There are many state-of-art algorithms in the literature that have been used for image optimization process in which they were inspired from the nature such as the Particle Swarm Optimization (PSO), Differential Evolution (DE) and more recently, the Cuckoo Optimization Algorithm (COA). COA is very efficient optimization technique developed by Yang and Deb through applying a special versions of gauss distribution for solving optimization problems. COA is differentiated from the life-style and the characteristics of Cuckoo sparrow clique. Cuckoo sparrow society initiates with an elementary inhabitance that is classified into two portions: cuckoos and eggs. The cuckoo societies then start to change their environment to better one and start reproducing and putting eggs. Such endeavor of Cuckoos to enhance their life’s environment is the Cuckoo Optimization Algorithm. In this chapter, a comprehensive discussion about one of the metaheuristic algorithms called Cuckoo Search Optimization (CSO) has been carried out. Also, the usefulness of CSO for solving image optimization problems is discussed in detail. Moreover, to support the theoretical discussion of CSO algorithm, the performance evaluation of CSO algorithm is provided in the results and comparisons section which compare and benchmark the execution of CSO algorithm with other genetic algorithms and particle swarm optimization. Finally, the analysis of comparison results illustrated the superior capability of Cuckoo search algorithm in optimizing the enhancement functions for digital image processing.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Publications, Upper Saddle River, NJ (2007)

    Google Scholar 

  2. Singh, N., Kaur, M., Singh, K.V.P.: Parameter optimization in image enhancement using PSO. Am. J. Eng. Res. (AJER) 2(5), 84–90. e-ISSN: 2320-0847, p-ISSN: 2320-0936

    Google Scholar 

  3. Munteanu, C., Rosa, A.: Towards automatic image enhancement using Genetic Algorithms. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 1535–1542. Instituto Superior Tecnico, University Tecnica de Lisboa, Portugal (2000)

    Google Scholar 

  4. Gupta, A., Tripathi, A., Bhateja, V.: De-speckling of SAR images via an improved anisotropic diffusion algorithm. In: Proceedings of (Springer) International Conference on Frontiers in Intelligent Computing Theory and Applications (FICTA 2012), Bhubaneswar, India. AISC, vol. 199, pp. 747–754 (2012)

    Google Scholar 

  5. Pal, S.K., Bhandari, D., Kundu, M.K.: Genetic algorithms for optimal image enhancement. Pattern Recogn. Lett. 15(3), 261–271 (1994)

    Article  Google Scholar 

  6. Kwok, N.M., Ha, Q.P., Liu, D., Fang, G.: Contrast enhancement and intensity preservation for gray-level images using multiobjective particle swarm optimization. IEEE Trans. Autom. Sci. Eng. 6(1), 145–155 (2009)

    Article  Google Scholar 

  7. Thampi, S.M., Gelbukh, A., Mukhopadhyay, J. (eds.): Advances in signal processing and intelligent recognition systems. Adv. Intell. Syst. Comput. (2014). https://doi.org/10.1007/978-3-319-04960-1_25

    Google Scholar 

  8. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)

    Article  MathSciNet  Google Scholar 

  9. Jaime, M., Beatriz, J., Salvador, S.: Towards no-reference of peak signal to noise ratio. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 4(1) (2013)

    Google Scholar 

  10. Lei, X., Hu, Q., Kong, X., Xiong, T.: Image enhancement using hybrid intelligent optimization. Opt. & Optoelectron. Technol. 341–344 (2014)

    Google Scholar 

  11. Gorai, A., Ghosh, A.: Gray-level image enhancement by particle swarm optimization, pp. 72–77 (2009)

    Google Scholar 

  12. Weigel, D., Elsmann, T., Babovsky, H., Kiessling, A., Kowarschik, R.: Combination of the resolution enhancing image inversion microscopy with digital holography. Opt. Commun. 291, 110–115 (2013). https://doi.org/10.1016/j.optcom.2012.10.072

    Article  Google Scholar 

  13. Lin, C.L.: An approach to adaptive infrared image enhancement for long-range surveillance. Infrared Phys. Technol. 54, 84–91 (2011). https://doi.org/10.1016/j.infrared.2011.01.001

    Article  Google Scholar 

  14. Zhao, W.: Adaptive image enhancement based on gravitational search algorithm. Procedia Eng. 15, 3288–3292 (2011). https://doi.org/10.1016/j.proeng.2011.08.617

    Article  Google Scholar 

  15. Zeng, M., Li, Y., Menga, Q., Yang, T., Liu, J.: Improving histogram-based image contrast enhancement using gray-level information histogram with application to X-ray images. Optik Int. J. Light Electron Opt. 123, 511–520 (2012). https://doi.org/10.1016/j.ijleo.2011.05.017

    Article  Google Scholar 

  16. Santhanam, T., Radhika, S.: A novel approach to classify noises in images using artificial neural network. J. Comput. Sci. 6, 506–510 (2010). https://doi.org/10.3844/jcssp.2010.506.510

    Article  Google Scholar 

  17. Garg, R., Mittal, B., Garg, S.: Histogram meequalization techniques for image enhancement. Int. J. Electron. Commun. Technol. 2, 107–111 (2011)

    Google Scholar 

  18. Pentapalli, V.V.G., Varma, R.K.P: Cuckoo Search Optimization and its Applications: A Review. CSE Department, MVGR College of Engineering, Vizianagaram, India1 Associate. Professor, CSE Department, MVGR College of Engineering, Vizianagaram, India

    Google Scholar 

  19. Babu, R.K., Sunitha, K.V.N.: Original Research Paper Enhancing Digital Images Through Cuckoo Search Algorithm in Combination with Morphological Operation (2014)

    Google Scholar 

  20. Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), India, pp. 210–214. IEEE Publications, USA (2009)

    Google Scholar 

  21. Prashar, P., Jain, N., Mahna, S.: Image optimization using Cuckoo search and levy flight algorithms. Int. J. Comput. Appl. (0975–8887) 178(4) (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Sciences Department, King Faisal University, P.O. Box 380, Ahsa, 31982, Saudi Arabia

    Noor A. Jebril

  2. Electrical Engineering Department, King Faisal University, P.O. Box 380, Ahsa, 31982, Saudi Arabia

    Qasem Abu Al-Haija

Authors
  1. Noor A. Jebril
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qasem Abu Al-Haija
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qasem Abu Al-Haija .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya University , Coimbatore, Tamil Nadu, India

    Jude Hemanth

  2. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad , Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jebril, N.A., Abu Al-Haija, Q. (2019). Cuckoo Optimization Algorithm (COA) for Image Processing. In: Hemanth, J., Balas, V. (eds) Nature Inspired Optimization Techniques for Image Processing Applications. Intelligent Systems Reference Library, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-319-96002-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-96002-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96001-2

  • Online ISBN: 978-3-319-96002-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation