Advertisement

Deep Underwater Image Enhancement Through Integration of Red Color Correction Based on Blue Color Channel and Global Contrast Stretching

  • Kamil Zakwan Mohd AzmiEmail author
  • Ahmad Shahrizan Abdul Ghani
  • Zulkifli Md Yusof
  • Zuwairie Ibrahim
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 538)

Abstract

Deep underwater images experience some complicated problems, such as low contrast and blue-green illumination effect due to light attenuation in water medium. These problems reduce the extraction rate of valuable information from the image. This paper proposes a new method of enhancing underwater image. The proposed method consists of two major steps. The first step is explicitly designed to minimize the effect of blue-green illumination. This technique operates by correcting the red color channel by taking into account the differences between the red color with blue color in term of total pixel value. The more significant the difference of total pixel value between these colors, the higher the pixel value will be added to improve the red color and vice versa. Then, the overall image contrast is improved through global contrast stretching technique that is applied to all color channels. Qualitative and quantitative evaluations prove the effectiveness of the proposed method.

Keywords

Image processing Red color correction Contrast stretching 

Notes

Acknowledgements

This work is partially supported by Universiti Malaysia Pahang research grant, RDU170392 entitled “Dual Image Fusion Technique for Enhancement of Underwater Image Contrast.”

References

  1. 1.
    Hitam, M.S., Awalludin, E.A., Wan Yussof, W.N.J., Bachok, Z.: Mixture contrast limited adaptive histogram equalization for underwater image enhancement. In: Proceeding of the IEEE International Conference on Computer Applications Technology (ICCAT), pp. 1–5 (2013)Google Scholar
  2. 2.
    Schettini, R., Corchs, S.: Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J. Adv. Signal Process. 1–14 (2010)Google Scholar
  3. 3.
    Buchsbaum, G.: A spatial processor model for object colour perception. J. Franklin Inst. 310(1), 1–26 (1980)CrossRefGoogle Scholar
  4. 4.
    Senthilkumaran, N., Thimmiaraja, J.: Histogram equalization for image enhancement using MRI brain images. In: 2014 World Congress on Computing and Communication Technologies, pp. 80–83 (2014)Google Scholar
  5. 5.
    Iqbal, K., Salam, R.A., Osman, A., Talib, A.Z.: Underwater image enhancement using an integrated colour model. Int. J. Comput. Sci. 34(2), 239–244 (2007)Google Scholar
  6. 6.
    Iqbal, K., Odetayo, M., James, A., Salam, R.A., Talib, A.Z.H.: Enhancing the low quality images using unsupervised colour correction method. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1703–1709 (2010)Google Scholar
  7. 7.
    Mohd Naim, M.J.N., Mat Isa, N.A.: Pixel distribution shifting color correction for digital color images. Appl. Soft Comput. 12(9), 2948–2962 (2012)CrossRefGoogle Scholar
  8. 8.
    Abdul Ghani, A.S., Mat Isa, N.A.: Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl. Soft Comput. 27, 219–230 (2015)CrossRefGoogle Scholar
  9. 9.
    Abdul Ghani, A.S., Raja Aris, R.S.N.A., Muhd Zain, M.L.: Unsupervised contrast correction for underwater image quality enhancement through integrated-intensity stretched-Rayleigh histograms. J. Telecommun. Electron. Comput. Eng. 8(3), 1–7 (2016)Google Scholar
  10. 10.
    Ye, Z.: Objective assessment of nonlinear segmentation approaches to gray level underwater images. ICGST J. Graph. Vis. Image Process. 9(II), 39–46 (2009)Google Scholar
  11. 11.
    Wu, J., Huang, H., Qiu, Y., Wu, H., Tian, J., Liu, J.: Remote sensing image fusion based on average gradient of wavelet transform. In: IEEE International Conference on Mechatronics and Automation, pp. 1817–1822 (2005)Google Scholar
  12. 12.
    Mittal, A., Soundararajan, R., Bovik, A.C.: Making a ‘completely blind’ image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Kamil Zakwan Mohd Azmi
    • 1
    Email author
  • Ahmad Shahrizan Abdul Ghani
    • 1
  • Zulkifli Md Yusof
    • 1
  • Zuwairie Ibrahim
    • 1
  1. 1.Faculty of Manufacturing EngineeringUniversiti Malaysia PahangPekanMalaysia

Personalised recommendations