Red Preserving Algorithm for Underwater Imaging

  • Chunbo MaEmail author
  • Jun Ao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 699)


The Gray World algorithm can remove the green or blue cast in underwater images. However, when one component of RGB is very little, it would lead to supersaturate and color distortion. In this paper, an improved Gray World algorithm, called Red Preserving is proposed. The minimum color component has been least changed, and the green or blue cast in the underwater images is suppressed. Experiments show that the proposed algorithm is simple and efficient. Compared to the classic Gray World algorithm, it can better suppress the cast and restore the images.


Gray World Green-blue cast Red Preserving Underwater Image processing 


  1. 1.
    Torres-Méndez, L.A., Dudek, G.: Color correction of underwater images for aquatic robot inspection. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds.) EMMCVPR 2005. LNCS, vol. 3757, pp. 60–73. Springer, Heidelberg (2005). doi: 10.1007/11585978_5 CrossRefGoogle Scholar
  2. 2.
    Jobson, D.J., Rahman, Z., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)CrossRefGoogle Scholar
  3. 3.
    Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a certer/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997)CrossRefGoogle Scholar
  4. 4.
    Funt, B., Barnard, K., Martin, L.: Is machine colour constancy good enough? In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 445–459. Springer, Heidelberg (1998). doi: 10.1007/BFb0055683 Google Scholar
  5. 5.
    Land, E.: The Retinex theory of color vision. Sci. Am. 237(6), 108–128 (1978)CrossRefGoogle Scholar
  6. 6.
    Hitam, M.S., Yussof, W.N.J.H.W., Awalludin, E.A., Bachok, Z.: Mixture contrast limited adaptive histogram equalization for underwater image enhancement. In: Proceedings of Computer Applications Technology (ICCA), pp. 1, 5, 20–22 (2013)Google Scholar
  7. 7.
    Shamsuddin, N.B., Wan, F.B.W.A., Baharudin, B.B., Kushairi, M.: Significance level of image enhancement techniques for underwater images. IVIC 1, 490–494 (2012)Google Scholar
  8. 8.
    Chiang, J.Y., Chen, Y.C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21(4), 1756–1769 (2011). A Publication of the IEEE Signal Processing SocietyMathSciNetCrossRefGoogle Scholar
  9. 9.
    Wen, H.C., Tian, Y.H., Huang, T.J., Gao, W.: Single underwater image enhancement with a new optical model. In: IEEE International Symposium on Circuits and Systems (ISCAS 2013), pp. 753–756 (2013)Google Scholar
  10. 10.
    Carlevaris-Bianco, N., Mohan, A., Eustice, R.M.: Initial results in underwater single image dehazing. Oceans 27, 1–8 (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Guangxi Key Laboratory of Precision Navigation Technology and ApplicationGuilin University of Electronic TechnologyGuilinPeople’s Republic of China

Personalised recommendations