Advertisement

Contrast Enhancement of RGB Color Images by Histogram Equalization of Color Vectors’ Intensities

  • Farid García-Lamont
  • Jair Cervantes
  • Asdrúbal López-Chau
  • Sergio Ruiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

The histogram equalization (HE) is a technique developed for image contrast enhancement of grayscale images. For RGB (Red, Green, Blue) color images, the HE is usually applied in the color channels separately; due to correlation between the color channels, the chromaticity of colors is modified. In order to overcome this problem, the colors of the image are mapped to different color spaces where the chromaticity and the intensity of colors are decoupled; then, the HE is applied in the intensity channel. Mapping colors between different color spaces may involve a huge computational load, because the mathematical operations are not linear. In this paper we present a proposal for contrast enhancement of RGB color images, without mapping the colors to different color spaces, where the HE is applied to the intensities of the color vectors. We show that the images obtained with our proposal are very similar to the images processed in the HSV (Hue, Saturation, Value) and L*a*b* color spaces.

Keywords

Color characterization Histogram equalization RGB images 

References

  1. 1.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)Google Scholar
  2. 2.
    Jahanirad, M., Wahab, A.W.A., Anuar, N.B.: An evolution of image source camera attribution approaches. Forensic Sci. Int. 262, 242–275 (2016)CrossRefGoogle Scholar
  3. 3.
    Nnolim, U.A.: An adaptive RGB colour enhancement formulation for logarithmic image processing-based algorithms. Opt. Int. J. Light Electron Opt. 154, 192–215 (2018)CrossRefGoogle Scholar
  4. 4.
    Jun, H., Inoue, K., Hara, K., Urahama, K.: Saturation improvement in hue-preserving color image enhancement without gamut problem. ICT Express (2017).  https://doi.org/10.1016/j.icte.2017.07.003
  5. 5.
    Qian, X., Han, L., Wang, Y., Wang, B.: Color contrast enhancement for color night vision based on color mapping. Infrared Phys. Technol. 57, 36–41 (2013)CrossRefGoogle Scholar
  6. 6.
    Zhang, H., Friits, J.E., Goldman, S.A.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst. 110(2), 260–280 (2008)CrossRefGoogle Scholar
  7. 7.
    Agarwal, M., Mahajan, R.: Medical image contrast enhancement using range limited weighted histogram equalization. Procedia Comput. Sci. 125, 149–156 (2018)CrossRefGoogle Scholar
  8. 8.
    Rajinikanth, V., Couceiro, M.S.: RGB histogram based color image segmentation using firefly algorithm. Procedia Comput. Sci. 46, 1449–1457 (2015)CrossRefGoogle Scholar
  9. 9.
    Pare, S., Kumar, A., Bajaj, V., Singh, G.K.: A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve. Appl. Soft Comput. 47, 76–102 (2016)CrossRefGoogle Scholar
  10. 10.
    Zhou, Z., Sang, N., Hu, X.: Global brightness and local contrast adaptive enhancement for low illumination color image. Opt. Int. J. Light Electron Opt. 125(6), 1795–1799 (2014)CrossRefGoogle Scholar
  11. 11.
    Xiao, B., Tang, H., Jiang, Y., Li, W., Wang, G.: Brightness and contrast controllable image enhancement based on histogram specification. Neurocomputing 275, 2798–2809 (2018)CrossRefGoogle Scholar
  12. 12.
    Tang, J.R., Isa, N.A.M.: Bi-histogram equalization using modified histogram bins. Appl. Soft Comput. 55, 31–43 (2017)CrossRefGoogle Scholar
  13. 13.
    Ong, S., Yeo, N., Lee, K., Venkatesh, Y., Cao, D.: Segmentation of color images using a two-stage self-organizing network. Image Vis. Comput. 20(4), 279–289 (2002)CrossRefGoogle Scholar
  14. 14.
    Paschos, G.: Perceptually uniform color spaces for color texture analysis: an empirical evaluation. IEEE Trans. Image Process. 10(6), 932–937 (2001)CrossRefGoogle Scholar
  15. 15.
    Rong, Z., Li, Z., Dong-nan, L.: Study of color heritage image enhancement algorithms based on histogram equalization. Opt. Int. J. Light Electron Opt. 126(24), 5665–5667 (2015)CrossRefGoogle Scholar
  16. 16.
    Li, X., Fang, M., Zhang, J.J., Wu, J.: Learning coupled classifiers with RGB images for RGB-D object recognition. Pattern Recognit. 61, 433–446 (2017)CrossRefGoogle Scholar
  17. 17.
    Grupt, B., Agarwal T.K.: New contrast enhancement approach for dark images with non-uniform illumination. Comput. Electr. Eng. (2017).  https://doi.org/10.1016/j.compeleceng.2017.09.007
  18. 18.
    Ghani, A.S.A., Isa, N.A.M.: Automatic system for improving under water image contrast and color through recursive adaptive histogram modification. Comput. Electron. Agric. 141, 181–195 (2017)CrossRefGoogle Scholar
  19. 19.
    Gu, Z., Ju, M., Zhang, D.: A novel retinex image enhancement approach via brightness channel prior and change of detail prior. Pattern Recognit. Image Anal. 27(2), 234–242 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Farid García-Lamont
    • 1
  • Jair Cervantes
    • 1
  • Asdrúbal López-Chau
    • 2
  • Sergio Ruiz
    • 1
  1. 1.Centro Universitario UAEM TexcocoUniversidad Autónoma del Estado de MéxicoTexcoco-Estado de MéxicoMexico
  2. 2.Centro Universitario UAEM ZumpangoUniversidad Autónoma del Estado de MéxicoZumpango-Estado de MéxicoMexico

Personalised recommendations