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An Efficient Algorithm for Gray Level Image Enhancement Using Cuckoo Search

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7677))

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Abstract

This paper presents an efficient algorithm for gray level image enhancement using Cuckoo search (CS). The results are compared with Particle Swarm Optimization (PSO). The basic idea is to treat image enhancement as an optimization problem and then solve it using CS. It is observed that the proposed method provides better results than existing techniques.

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References

  1. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, New York (1987)

    Google Scholar 

  2. Munteanu, C., Rosa, A.: Evolutionary image enhancement with user behavior modeling. ACM SIGAPP Applied Computing Review 9(1), 8–14 (2001)

    Article  Google Scholar 

  3. Saitoh, F.: Image contrast enhancement using genetic algorithm. In: Proc. IEEE SMC, Tokyo, Japan, pp. 899–904 (1993)

    Google Scholar 

  4. Pal, S.K., Bhandari, D., Kundu, M.K.: Genetic algorithms for optimal image enhancement. Pattern Recognition Letter 15, 261–271 (1994)

    Article  MATH  Google Scholar 

  5. Jingquan, S., Mengyin, F., Chanjian, Z.: An image enhancement algorithm based on chaotic optimization. Computer Engineering and Applications 27, 4–6 (2003)

    Google Scholar 

  6. dos Santos Coelho, L., Sauer, J.G., Rudek, M.: Differential evolution optimization combined with chaotic sequences for image contrast enhancement. Chaos, Solitons and Fractals 42, 522–529 (2009)

    Article  Google Scholar 

  7. Yang, X.S., Deb, S.: Engineering optimization by cuckoo search. Int. J. Mathematical Modelling and Numerical Optimization 1(4), 330–343 (2010)

    Article  MATH  Google Scholar 

  8. Munteanu, C., Rosa, A.: Gray-scale enhancement as an automatic process driven by evolution. IEEE Transaction on Systems, Man and Cybernetics-Part B: Cybernetics 34(2), 1292–1298 (2004)

    Article  Google Scholar 

  9. Gorai, A., Ghosh, A.: Gray level Image enhancement by Particle Swarm optimization. In: World Congress on Nature & Biologically Inspired Computing, pp. 72–77 (2009)

    Google Scholar 

  10. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(8), 2378–2386 (2011)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics & Telecommunication Engineering, VSS University of Technology, Burla, Odisha, India

    Sanjay Agrawal & Rutuparna Panda

Authors
  1. Sanjay Agrawal
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  2. Rutuparna Panda
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, 110016, Delhi, India

    Bijaya Ketan Panigrahi

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electronics and Telecom. Engineering, Institute of Technical Education & Research, Siksha ’O’ Anusandhan University, 751030, Bhubaneswar, Odisha, India

    Pradipta Kumar Nanda

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© 2012 Springer-Verlag Berlin Heidelberg

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Agrawal, S., Panda, R. (2012). An Efficient Algorithm for Gray Level Image Enhancement Using Cuckoo Search. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_11

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  • DOI: https://doi.org/10.1007/978-3-642-35380-2_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35379-6

  • Online ISBN: 978-3-642-35380-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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