Advertisement

Particle Swarm Optimization-Based SONAR Image Enhancement for Underwater Target Detection

  • P. M. Rajeshwari
  • G. Kavitha
  • C. M. Sujatha
  • Dhilsha Rajapan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)

Abstract

In the proposed work, particle swarm optimization (PSO) is employed to enhance SONAR images in spatial domain. A transformation function is used with the local and global data of the image to enhance the image based on PSO. The objective function considers the entropy of the image, number of edges detected, and the sum of edge intensities. PSO determines the optimal parameters required for image enhancement. PSO-based image enhancement is compared with median filter and adaptive Wiener filter both qualitatively and quantitatively. Mean square error (MSE) and peak signal-to-noise ratio (PSNR) are used as quantitative measures for the analysis of the enhanced images. MSE and PSNR analysis reveals that original details of the image are retained after enhancement. Based on the visual and quantitative analysis, it is considered that PSO-based technique provides better enhancement of SONAR images.

Keywords

Particle swarm optimization Mean square error PSNR Ant colony optimization Artificial bee colony method 

Notes

Acknowledgments

Authors would like to acknowledge M/s Edge Tech for image courtesy wreck, bridge support, and hurricane gate, and the Director, NIOT, for encouragement and permitting to publish the work.

References

  1. 1.
    P. Shanmugavadivu, K. Balasubramanian, K. Somasundaram, Modified histogram equalization for image contrast enhancement using particle swarm optimisation. Int. J. Comput. Sci. Eng. Infor. Technol. 1, 13–27 (2011)Google Scholar
  2. 2.
    G. Padmavathi, P. Subashini, M. Kumar, S.K. Thakur, Performance analysis of non linear filtering algorithms for underwater images. Int. J. Comput. Sci. Infor. Secur. 6, 232–238 (2009)Google Scholar
  3. 3.
    J. Alavandan, S. Santhosh Baboo, Enhanced speckle filters for SONAR images using stationary wavelet and hybrid inter- and intra-scale wavelet coefficient dependency. Global J. Comput. Sci. Technol. 12, 12–19 (2012)Google Scholar
  4. 4.
    S. Sulochana, R. Vidhya, Image denoising using adaptive thresholding in framelet transform domain. Int. J. Adv. Comput. Sci. Appl. 3, 192–196 (2012)Google Scholar
  5. 5.
    Y.A. Al-Sbou, Artificial neural networks evaluation as an image denoising tool. World Appl. Sci. J. 17, 218–227 (2012)Google Scholar
  6. 6.
    V. Soni, A.K. Bhandari, A. Kumar, G.K. Singh, Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms. IET Sig. Process. 7, 720–730 (2013)CrossRefGoogle Scholar
  7. 7.
    M. Braik, A. Sheta, Aladdin Ayesh: image enhancement using particle swarm optimization. Proc. World Congr. Eng. 01, 696–701 (2011)Google Scholar
  8. 8.
    A. Gorai, A. Ghosh, Gray-level image enhancement by particle swarm optimization, in World Congress on Nature and Biologically Inspired Computing, pp. 72–76 (2009)Google Scholar
  9. 9.
    T. Zhang, L. Wan, Y. Xu, Y. Lu, Sonar image enhancement based on particle swarm optimisation, IN 3rd IEEE Conference on Industrial Electronics and Applications (2008), pp. 2216–2221Google Scholar
  10. 10.
    J.C. Bansal, P.K. Singh, M. Saraswat, A. Verma, S.S. Jadon, A. Abraham, Inertia weight strategies in particle swarm optimization, in Third World Congress on Nature and Biologically Inspired Computing (2011), pp. 633–640Google Scholar
  11. 11.
    A.M. Amira, S.E.L. Rabaie, T.E. Taha, O. Zahran, F.E. Abd El-Samie, Comparative study of different denoising filters for speckle noise reduction in ultrasonic b-mode images. Int. J. Image Graph. Sig. Process. 2, 1–8 (2013)Google Scholar
  12. 12.
    H.Y. Chai, E. Supriyanto, L.K. Wee, MRI brain tumor image segmentation using region-based active contour model. Latest trends in applied computational science, in Proceedings of the 12th International Conference on Applied Computer and Applied Computational Science (2013), pp. 36–41Google Scholar
  13. 13.
    A. Adeli, F. Tajeripoor, M.J. Zomorodian, M. Neshat, Comparison of the fuzzy-based wavelet shrinkage image denoising techniques. Int. J. Comput. Sci. Issues 9, 211–216 (2012)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • P. M. Rajeshwari
    • 1
  • G. Kavitha
    • 2
  • C. M. Sujatha
    • 3
  • Dhilsha Rajapan
    • 1
  1. 1.Marine Sensors SystemsNational Institute of Ocean TechnologyChennaiIndia
  2. 2.Madras Institute of TechnologyAnna UniversityChennaiIndia
  3. 3.College of Engineering, GuindyAnna UniversityChennaiIndia

Personalised recommendations