International Journal of Naval Architecture and Ocean Engineering

  • 제6권4호
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  • Pages.840-866
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  • 2014
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  • 2092-6782(pISSN)
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  • 2092-6790(eISSN)
대한조선학회 (The Society of Naval Architects of Korea)
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Underwater image quality enhancement through Rayleigh-stretching and averaging image planes

  • Ghani, Ahmad Shahrizan Abdul (Imaging and Intelligent System Research Team (ISRT), School of Electrical & Electronics Engineering, Engineering Campus, Universiti Sains Malaysia) ;
  • Isa, Nor Ashidi Mat (Imaging and Intelligent System Research Team (ISRT), School of Electrical & Electronics Engineering, Engineering Campus, Universiti Sains Malaysia)
  • 발행 : 2014.12.31
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초록

Visibility in underwater images is usually poor because of the attenuation of light in the water that causes low contrast and color variation. In this paper, a new approach for underwater image quality improvement is presented. The proposed method aims to improve underwater image contrast, increase image details, and reduce noise by applying a new method of using contrast stretching to produce two different images with different contrasts. The proposed method integrates the modification of the image histogram in two main color models, RGB and HSV. The histograms of the color channel in the RGB color model are modified and remapped to follow the Rayleigh distribution within certain ranges. The image is then converted to the HSV color model, and the S and V components are modified within a certain limit. Qualitative and quantitative analyses indicate that the proposed method outperforms other state-of-the-art methods in terms of contrast, details, and noise reduction. The image color also shows much improvement.

키워드

과제정보

연구 과제번호 : The Investigation of New Color Image Illumination Estimation Concept for the Development of New Color-Correction Techniques

연구 과제 주관 기관 : Ministry of Higher Education of Malaysia (MOHE)

참고문헌

  1. Arici, T., Dikbas, S. and Altunbasak, Y., 2009. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on Image Processing, 18(9), pp.1921-1935. https://doi.org/10.1109/TIP.2009.2021548
  2. Church, S.C. and White, E.M., 2003. Ultraviolet dermal reflection and mate choice in the guppy. Elsevier Sciences Ltd, 65(4), pp.693-700.
  3. Eustice, R., Pizarro, O., Singh, H. and Howland, J., 2002. Underwater image toolbox for optical image processing and mosaicking in MATLAB. Proceedings International Symposium on Underwater Technology, Tokyo, 19 April 2002, pp. 141-145.
  4. Gasparini, F. and Schettini, R., 2003. Color correction for digital photographs. 12th International Conference on Image Analysis and Processing (ICIAP 2003), Mantova, 17-19 September 2003, pp.646-651.
  5. Hitam, M.S., Yussof, W.N.J.W. and Awalludin, E.A., 2013. Mixture contrast limited adaptive histogram equalization for underwater image enhancement. IEEE International Conference on Computer Applications Technology (ICCAT), Sousse, 20-22 January 2013, pp.1-5.
  6. Iqbal, K., Odetayo, M., James, A., Salam, R.A. and Talib, A.Z., 2010. Enhancing the low quality images using unsupervised color correction method. International Conference on System Man and Cybernatics (SMC), Istanbul, 10- 13 October 2010, pp.1703-1709.
  7. Iqbal, K., Salam, R.A., Osman, A. and Talib, A.Z., 2007. Underwater image enhancement using integrated color model. IAENG International Journal of Computer Science, 34(2), pp.529-534.
  8. Jaffe, J.S., 1990. Computer modeling and the design of optimal underwater imaging systems. IEEE Journal of Oceanic Engineering, 15(2), pp.101-111. https://doi.org/10.1109/48.50695
  9. Kaushik, P. and Sharma, Y., 2012. Comaprison of different image enhancement technique based upon PSNR and MSE. International Journal of Applied Engineering Research, 7(11), pp.2010-2014.
  10. King-Smith, P.E. and Kulikowski, J.J., 1975. Pattern and flicker detection analyzed by subthreshold summation. Journal of Physiology, 249, pp.519-548. https://doi.org/10.1113/jphysiol.1975.sp011028
  11. Kumar, R., Rattan, M., 2012. Analysis of various uality metrics for medical image processing. International Journal of Advance Research on Computer Science and Software Engineering, 2(11), pp.137-144.
  12. Legris, M., Lebart, K., Fohanno, F. and Zerr, B., 2003. Les capteurs d'imagerie en robotique sous-marine: tendances actuelles et futures. Traitement du Signal, 20, pp.137-164.
  13. McGlamery, B.L., 1979. A computer model for underwater camera systems. Proceeding of the SPIE Ocean Optics, 208, pp.221-231.
  14. Michelson, A.A., 1927. Studies in optics. Chicago: University of Chicago Press.
  15. Naim, M.J.N.M. and Isa, N.A.M., 2012. Pixel distribution shifting color correction for digital color images. Journal of Applied Soft Computing, 12(9), pp.2948-2962. https://doi.org/10.1016/j.asoc.2012.04.028
  16. Ndajah, P., Kikuchi, H., Yukawa, M., Watanabe, H. and Muramatsu, S., 2011. An Investigation on the quality of denoised images. International Journal of Circuit, Systems, and Signal Processing. 5(4), pp.423-434.
  17. Padmavathi, G., Muthukumar, M. and Thakur, S.K., 2010. Non linear image segmentation using fuzzy c-means clustering method with thresholding for underwater images. International Journal of Computer Sciences Issues (IJCSI), 7(3), pp.35-40.
  18. Reinagel, P. and Zador, A.M., 1999. Natural scene statistics at the center of gaze. Network: Computation in Neural Systems, 10, pp.1-10. https://doi.org/10.1088/0954-898X/10/1/001
  19. Rizzi, A. and Gatta, C. and Marini, D., 2003. A new algorithm for unsupervised global and local color correction. Pattern Recognition Letters, 24(11), pp.1663-1677 https://doi.org/10.1016/S0167-8655(02)00323-9
  20. Rizzi, A., Algeri, T., Medeghini, G. and Marini, D., 2004. A Proposal for contrast measure in digital images. Proceeding of the Second European Conference on Color in Graphics, Imaging, and Vision (CGIV), Aachen, 5-8 April 2004, pp.187- 192.
  21. Schettini, R. and Corchs, S., 2012. Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP Journal on Advances in Signal Processing, 2010:746052.
  22. Schechner, Y.Y. and Karpel, N., 2004. Clear underwater vision. Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, 27 June - 2 July 2004, pp. I-536 - I-543.
  23. Schechner, Y.Y. and Karpel, N., 2005. Recovery of underwater visibility and structure by polarization Analysis. IEEE Journal of Oceanic Engineering, 30(3), pp.570-587. https://doi.org/10.1109/JOE.2005.850871
  24. Senthilkumaran, N. and Thimmiaraja, J., 2014. Histogram equalization for image enhancement using MRI brain images. IEEE World Congress on Computing and Communication Tchnologies, Trichirappalli, 27 February - 1 March 2014, pp.80-83.
  25. Shamsudin, N., Ahmad, W.F.W., Baharudin, B., Rajuddin, M.K.M., 2012. Significance level of image enhancement techniques for underwater images. International Conference on Computer & Information Sciences (ICCIS), Kuala Lumpur, 12-14 June 2012, pp.490-494.
  26. Singhai, J. and Rawat, P., 2007. Image enhancement method for underwater, ground, and satellite image using brightness preserving histogram equalization with maximum entropy. IEEE International Conference on Computational Intelligent and Multimedia Application (ICCIMA), Tamil Nadu, 13-15 December 2007, pp. 507-512.
  27. Trucco, E. and Olmos-Antillon, A.T., 2006. Self-tuning underwater image restoration. IEEE Journal of Oceanic Engineering, 31(2), pp.511-519. https://doi.org/10.1109/JOE.2004.836395
  28. Whittle, P., 1986. Increments and decrements: luminance discrimination. Vision Research, 26(10), pp.1677-1691. https://doi.org/10.1016/0042-6989(86)90055-6
  29. Wu, J., Huang, H., Qiu, Y., Wu, H., Tian, J. and Liu, J., 2005. Remote sensing image fusion based on average gradient of wavelet transform. Proceeding of the IEEE: International Conference on Mechatronics & Automation Niagara Falls, Canada, July 2005, pp.1817-1821.
  30. Wu, X. and Li, H., 2013. A simple and comprehensive model for underwater image restoration. Proceeding of the IEEE, International Conference on Information and Automation, Yinchuan, 26-28 August 2013, pp.699-704.
  31. Ye, Z., 2009. Objective assessment of nonlinear segmentation approaches to gray level underwater images. International Journal on graphics, Vision, and Image Processing (GVIP), 9(2), pp.39-46.
  32. Yeganeh, H., Ziaei, A. and Rezaei, A., 2008. A novel approach for contrast enhancement based on histogram equalization. IEEE Proceedings of International Conference on Computer and communication Engineering, Kuala Lumpur, 13-15 May 2008, pp.256-260.