Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan

CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.188
Source Normalized Impact per Paper (SNIP) 2018: 0.533

See all formats and pricing
More options …
Volume 24, Issue 1


Underwater Image Enhancement Using Particle Swarm Optimization

Amal AbuNaser / Iyad Abu Doush
  • Corresponding author
  • Department of Computer Sciences, College of Information Technology and Computer Sciences, Yarmouk University, 21163 Irbid, Jordan
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Nahed Mansour / Sawsan Alshattnawi
Published Online: 2014-08-07 | DOI: https://doi.org/10.1515/jisys-2014-0012


This article introduces a framework for enhancing underwater images using the particle swarm optimization algorithm. A pre-processing step is introduced to reduce the absorbing and scattering effects of water before applying a filter based on this algorithm to enhance the image. The quality of enhanced images is quantitatively assessed by applying the framework on a dataset of underwater images. The obtained results show a considerable improvement.

Keywords: Underwater images; particle swarm optimization; underwater image enhancement; Kullback–Leibler divergence; histogram; peak signal-to-noise ratio; number of edges


  • [1]

    A. Arnold-Bos, J. P. Malkasse and G. Kervern, A preprocessing framework for automatic underwater images denoising, in: Proceedings of the European Conference on Propagation and Systems 2005, Brest, France, 2005.Google Scholar

  • [2]

    A. Arnold-Bos, J. P. Malkasse and G. Kervern, Towards a model-free denoising of underwater optical images, in: Oceans 2005 – Europe, vol. 1, pp. 527–532, 2005.Google Scholar

  • [3]

    S. Bakhtiari, S. Agaian and M. Jamshidi, A color image enhancement method based on ensemble empirical mode decomposition and genetic algorithm, in: World Automation Congress (WAC), 2012, pp. 1–6, 2012.Google Scholar

  • [4]

    S. Bazeille, I. Quidu, L. Jaulin and J. Malkasse, Automatic underwater image pre-processing, in: Proceedings of the Caracterisation du Milieu Marin (CMM ‘06), 2006, 1–8.Google Scholar

  • [5]

    M. Braik and A. Sheta, Image enhancement using particle swarm optimization, in: Lecture Notes in Engineering and Computer Science, 2007, 696–701.Google Scholar

  • [6]

    F. Canny, A computational approach to edge detection, J. IEEE Pattern Anal. Machine Intell. 8 (1986), 679–698.Google Scholar

  • [7]

    M. Chambah, D. Semani, A. Renouf, P. Courtellemont, A. Rizzi and L. Rochelle, Underwater color constancy: enhancement of automatic live fish recognition, in: SPIE/IS&T Electronic Imaging 2004, 2004.Google Scholar

  • [8]

    J. Chiang, Y. C. Chen and Y. F. Chen, Underwater image enhancement: using wavelength compensation and image dehazing (WCID), in: Advanced Concepts for Intelligent Vision System, vol. 6915, pp. 372–383, Springer-Verlag, Berlin, Heidelberg, 2011.Google Scholar

  • [9]

    L. Coelho, J. Sauer and M. Rudek, Differential evolution optimization combined with chaotic sequences for image contrast enhancement, Chaos Solitons Fractals 42 (2009), 522–529.CrossrefWeb of ScienceGoogle Scholar

  • [10]

    X. Cufi, R. Garcia and P. Ridao, An approach to vision-based station keeping for an unmanned underwater vehicle, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, vol. 1., pp. 799–804, 2002.Google Scholar

  • [11]

    Y. Del Valle, G. K. Venayagamoorthy, S. Mohagheghi, J. C. Hernandez and R. G. Harley, Particle swarm optimization: basic concepts, variants and applications in power systems, IEEE Trans. Evol. Comput. 12 (2008), 171–195.Web of ScienceCrossrefGoogle Scholar

  • [12]

    D. Eriksson, L. Eriksson, E. Frisk and M. Krysander, Analysis and optimization with the Kullback–Leibler divergence for misfire detection using estimated torque, Technical Report LiTH-R-3057, Department of Electrical Engineering, Linköpings Universitet, Linköping, Sweden, 2013.Google Scholar

  • [13]

    A. Fairweather, M. Hodgetts and A. Greig, Robust scene interpretation of underwater image sequences, in: 6th International Conference on Image Processing and Its Applications, pp. 660–664, 1997.Google Scholar

  • [14]

    A. Ford and A. Roberts, Colour space conversions, 1998 (technical report ).Google Scholar

  • [15]

    W. Gao, X. Zhang, L. Yang and H. Liu, An improved Sobel edge detection, in: 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010), vol. 5, pp. 67–71, 2010.Google Scholar

  • [16]

    F. Gasparini and R. Schettini, Color correction for digital photographs, in: Proceedings of the 12th International Conference on Image Analysis and Processing, ICIAP ‘03, IEEE Computer Society, pp. 646–651, Washington, DC, 2003.Google Scholar

  • [17]

    R. Gonzales and R. E. Woods, Digital Image Processing, Upper Saddle River, NJ: Pearson Prentice Hall, 2001.Google Scholar

  • [18]

    A. Gorai and A. Ghosh, Gray-level image enhancement by particle swarm optimization, in: World Congress on Nature Biologically Inspired Computing, 2009, NaBIC 2009, pp. 72–77, 2009.Google Scholar

  • [19]

    A. Gorai and A. Ghosh, Hue-preserving color image enhancement using particle swarm optimization, in: 2011 IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 563–568, 2011.Google Scholar

  • [20]

    K. Iqbal, A. Rosalina, A. Osman and A. Talib, Underwater image enhancement using an integrated colour model, IAENG Int. J. Comput. Sci. 34 (2007), 239–244.Google Scholar

  • [21]

    K. Iqbal, M. Odetayo, A. James, R. Salam and A. Talib, Enhancing the low quality images using unsupervised colour correction method, in: Systems 2010 IEEE International Conference on Man and Cybernetics (SMC), pp. 1703–1709, 2010.Google Scholar

  • [22]

    D. Johnson and S. Sinanovic, Symmetrizing the Kullback–Leibler distance, Technical Report, IEEE Trans. Inform. Theory (2000), 1–10.Google Scholar

  • [23]

    N. Kansal, Fuzzy techniques for image enhancement, Ph.D. thesis, Thepar University, Patiala, Punjab, India, 2010.Google Scholar

  • [24]

    Kullback–Leibler divergence, World Wide Web electronic publication (2007). http://www.mathworks.com/matlabcentral/fileexchange/13089-kldiv/content/kldiv.m.

  • [25]

    C. Munteanu and A. Rosa, Towards automatic image enhancement using genetic algorithms, in: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2., pp. 1535–1542, 2000.Google Scholar

  • [26]

    C. Munteanu and A. Rosa, Gray-scale image enhancement as an automatic process driven by evolution, IEEE Trans. Syst. Man Cybern. B 34 (2004), 1292–1298.Google Scholar

  • [27]

    G. Padmavathi, P. Subashini, M. Kumar and S. Thakur, Performance analysis of non linear filtering algorithms for underwater images, IJCSI 6 (2009), 232–238.Google Scholar

  • [28]

    G. Padmavathi, P. Subashini, M. Kumar and S. Thakur, Comparison of filters used for underwater image pre-processing, Int. J. Comput. Sci. Network Secur. 10 (2010), 58–64.Google Scholar

  • [29]

    Peak signal to noise ratio. World Wide Web electronic publication (2012). http://www.mathworks.com/matlabcentral/fileexchange/37691-psnr-for-rgb-images/content/PSNR_RGB.m.

  • [30]

    J. Perkio and A. Hyvarinen, Modelling image complexity by independent component analysis, with application to content-based image retrieval, in: C. Alippi, M. M. Polycarpou, C. G. Panayiotou and G. Ellinas, eds., ICANN (2), Lecture Notes in Computer Science, pp. 704–714, Springer-Verlag, Berlin, Heidelberg, vol. 5769, 2009.Google Scholar

  • [31]

    C. J. Prabhakar and P. U. Praveen Kumar, An image based technique for enhancement of underwater images, Int. J. Mach. Inte. 3 (2011), 217–224.Google Scholar

  • [32]

    T. Prabhakar, V. Naveen, A. Prasanthi and G. Santhi, Image compression using dct and wavelet transformations, Int. J. Signal Process. Image Process. Pattern Recognit. 4 (2011), 61–74.Google Scholar

  • [33]

    G. Ramponi, N. Strobel, S. Mitra and T. H. Yu, Nonlinear unsharp masking methods for image contrast enhancement, J. Electron. Imaging 5 (1996), 353–366.Google Scholar

  • [34]

    C. Ren and J. Yang, A novel color microscope image enhancement method based on HSV color space and curvelet transform, IJCSI 9 (2012), 272–277.Google Scholar

  • [35]

    Y. Ren, J. Li and W. Bi, A study on underwater sensing image enhancement based on differential evolution, Sensor Lett. 10 (2012), 1552–1556.Web of ScienceCrossrefGoogle Scholar

  • [36]

    A. Rizzi, C. Gatta and D. Marini, A new algorithm for unsupervised global and local color correction, in: Colour Image Processing and Analysis. First European Conference on Colour in Graphics, Imaging, and Vision (CGIV 2002), Pattern Recognit. Lett. 24 (2003), 1663–1677.Google Scholar

  • [37]

    P. Rosin, Edges: saliency measures and automatic thresholding, Mach. Vision Appl. 9 (1997), 139–159.CrossrefGoogle Scholar

  • [38]

    Y. Y. Schechner and N. Karpel, Clear underwater vision, in: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 1, pp. 536–543, 2004.Google Scholar

  • [39]

    N. B. Shamsuddin, W. F. B. Wan Ahmad, B. Baharudin, M. K. B. Mohd Rajuddin and F. B. Mohd, Image enhancement of underwater habitat using color correction based on histogram, in: IVIC (1), Lecture Notes in Computer Science, pp. 289–299, Springer-Verlag, Berlin, Heidelberg, vol. 7066, 2011.Google Scholar

  • [40]

    H. Talebi and N. Esmailzadeh, Using Kullback–Leibler distance for performance evaluation of search designs, Bull. Iran. Math. Soc. 37 (2011), 269–279.Google Scholar

  • [41]

    L. Torres-Méndez and G. Dudek, Color correction of underwater images for aquatic robot inspection, in: A. Rangarajan, B. Vemuri and A. Yuille, eds., Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Springer-Verlag, Berlin, Heidelberg, vol. 3757, pp. 60–73, 2005.Google Scholar

  • [42]

    I. Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection, Inform. Process. Lett. 85 (2003), 317–325.CrossrefWeb of ScienceGoogle Scholar

  • [43]

    Underwater images, World Wide Web electronic publication (2002). http://www.dive.snoack.de/tutorials/g_Tutorial_01.html#Photo1a.

About the article

Corresponding author: Iyad Abu Doush, Department of Computer Sciences, College of Information Technology and Computer Sciences, Yarmouk University, 21163 Irbid, Jordan, e-mail:

Received: 2014-02-06

Published Online: 2014-08-07

Published in Print: 2015-03-01

Citation Information: Journal of Intelligent Systems, Volume 24, Issue 1, Pages 99–115, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2014-0012.

Export Citation

©2015 by De Gruyter.Get Permission

Comments (0)

Please log in or register to comment.
Log in