A Chaotic Lévy flight Approach in Bat and Firefly Algorithm for Gray level image Enhancement

Автор: Krishna Gopal Dhal, Iqbal Quraishi, Sanjoy Das

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 7 vol.7, 2015 года.

Бесплатный доступ

Recently nature inspired metaheuristic algorithms have been applied in image enhancement field to enhance the low contrast images in a control manner. Bat algorithm (BA) and Firefly algorithm (FA) is one of the most powerful metaheuristic algorithms. In this paper these two algorithms have been implemented with the help of chaotic sequence and lévy flight. One of them is FA via lévy flight where step size of lévy flight has been taken from chaotic sequence. In the Bat algorithm the local search has been done via lévy flight with chaotic step size. Chaotic sequence shows ergodicity property which helps in better searching. These two algorithms have been applied to optimize parameters of parameterized high boost filter. Entropy, number of edge pixels of the image have been used as objective criterion for measuring goodness of image enhancement. Fitness criterion has been maximized in order to get enhanced image with better contrast. From the experimental results it is clear that BA with chaotic lévy outperforms the FA via chaotic lévy.

Еще

Image enhancement, Bat algorithm, Firefly algorithm, Lévy flight, Chaotic sequence

Короткий адрес: https://readera.ru/15013892

IDR: 15013892

Список литературы A Chaotic Lévy flight Approach in Bat and Firefly Algorithm for Gray level image Enhancement

  • Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, New York (2002)
  • Gorai, A., Ghosh, A.: Gray-level Image Enhancement By Particle Swarm Optimization. In Proceedings of World Congress on Nature & Biologically Inspired Computing.( 2009)
  • Gorai, A., Ghosh, A.: Hue preserving color Image Enhancement By Particle Swarm Optimization.IEEE,pp. 563-568,(2011)
  • Garg, R., Mittal, B., Garg, S.:Histogram Equalization Techniques For Image Enhancement. International Journal of electronics and communication technology.2,107-111(2011).
  • Yang, S., Oh, J.H., Park, Y.: Contrast enhancement using Histogram Equalizationwith bin underflow and bin overflw. ICIP (2003).
  • Yang, X. S.: Firefly Algorithm, Lévy Flights and Global Optimization. Research and Development in Intelligent Systems (2010). Doi:10.1007/978-1-84882-983-1_15.
  • Yang, X.S.: Engineering Optimization: An Introduction to Metaheuristic Applications. Wiley, Hoboken, New Jersey (2010).
  • Pal, S., K., Bhandari, D., Kundu, M., K.:Genetic algorithms for optimal image enhancement.Pattern Recognition Letters. 15, 261-271 (1994).
  • Hashemi, S., Kiani, S., Noroozi, N., Moghaddam, M. E.: An image contrast enhancement method based on genetic algorithm. Pattern Recognition Letters. 31, 1816–1824(2010).
  • Yun-Fei, C., Yong-Hao, X., Wei-Yu, Y., Yong-Chang, C.: Multi-level Threshold Image Segmentation Based on PSNR using Artificial Bee Colony Algorithm. Research Journal of Applied Sciences Engineering and Technology. 4, 104-107 (2012).
  • Ma, M., Liang, J., Guo, M., Fan, Y., Yin, Y.: SAR image segmentation based on Artificial Bee Colony algorithm. Applied Soft Computing.11, 5205–5214(2011).
  • Coelho, L. D. S., Sauer, J.G., Rudek, M.: Differential evolution optimization combined with chaotic sequences for image contrast enhancement. Chaos, Solitons and Fractals .42, 522–529(2009).
  • Braik, M., Sheta, A., Ayesh, A.: Image Enhancement Using Particle Swarm Optimization. Proceedings of the World Congress on Engineering.(2007).
  • Shanmugavadivu, P., Balasubramanian, K., Muruganandam, A.: Particle swarm optimized bi-histogram equalization for contrast enhancement and brightness preservation of images. Vis Comput (2014). doi:10.1007/s00371-013-0863-8.
  • Gupta, K., Gupta, A.: Image Enhancement using Ant Colony Optimization. IOSR Journal of VLSI and Signal Processing. 1, 38-(2012).
  • Sheikholeslami, R., Kaveh, A.: A Survey of Chaos Embedded Meta-Heuristic Algorithms. Int. J. Optim. Civil. Eng. 3(4), 617-633 (2013).
  • Yang, X., S.: A New Metaheuristic Bat-Insspired Algorithm. Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligence (Springer). pp- 65-74 (2010).
  • Yang, X., S.: Bat Algorithm for Multi-Objective Optimization. Int. J. of Bio-Inspired Computation.3, 267-274 (2011).
  • Jamil, M., Zepernick, H. J.: Lévy Flights and Global Optimization. Bio-Inspired Computation (2013). doi:http://dx.doi.org/10.1016/B978-0-12-405163-8.00003-X.
  • Boccaletti, S., Grebogi, C., Lai, Y., C., Mancini, H., Maza, D.:The control of chaos: Theory and applications. Physics Reports: 329,103-197 (2000).
  • Leandro, C., S., d., Viviana, C., M.: A novel particle swarm optimization approach using Henon map and implicit filtering local search for economic load dispatch. Chaos, Solitons and Fractals. 39,510-518 (2009).
  • Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M., G.: Chaotic Sequences to Improve the Performance of Evolutionary Algorithms. IEEE Transaction on Evolutionary Computation. 7, 289-304 (2003).
  • Coelho, L. d. S., Mariani, V. C.: Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Systems with Applications. 34, 1905-1913 (2008).
  • Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010).
  • Yang, X. S., Deb, S.: Engineering Optimisation by Cuckoo Search. Int. J. Mathematical Modelling and Numerical Optimisation.1, 330-343(2010).
  • Leccardim, M.: Comparison of three algorithm for Lévy noise generation. PACS: 05.10.Ln, 89.75 Da, 05.45. Tp.
  • Walton, S., Hassan, O., Morgan, K., Brown, M. R.:A Review of the Development and applications of the Cuckoo Search Algorithm. Swarm Intelligence and Bio-Inspired Computation (2013). doi:http://dx.doi.org/10.1016/B978-0-12-405163-8.00011-9.
  • Jha, R., K., Chouhan, R.: Noise-induced contrast enhancement using stochastic resonance on singular values. SIViP (2014). Springer. vol- 8, pp-339-347. DOI 10.1007/s11760-012-0296-2.
  • Chaudhury, S., Roy, K., A.: Histogram Equalization- A Simple but Efficient Technique for Image Enhancement. I.J. Image, Graphics and Signal Processing (2013), MECS, 10, pp-55-62, DOI 10.5815/ijigsp.2013.10.07.
  • Singh, P., D., Khare, A.: Evolutionary image enhancement using Multi- Objective Genetic Algorithm. I.J. Image, Graphics and Signal Processing (2014), MECS, 1, pp-61-67, DOI: 10.5815/ijigsp.2014.01.09.
  • Chaurasia, P., O.: An Approach to Fingerprint Image Pre-Processing. I.J. Image, Graphics and Signal Processing (2012), MECS, 6, PP- 29-35. DOI: 10.5815/ijigsp.2012.06.05.
  • Bansal, J., C., Singh, P., K., Saraswat, M., Verma, A., Jadon, S.,S., Abraham, A.: Inertia weight strategies in Particle swarm optimization. Third World Congress on Nature and Biologically Inspired Computing (2011). pp- 640-647.
Еще
Статья научная