Advertisement

Optical Review

, Volume 24, Issue 3, pp 406–415 | Cite as

Gradient-norm-based histogram equalization taking account of HSV color space distribution

  • Yoshiaki Ueda
  • Takanori Koga
  • Hideaki Misawa
  • Noriaki Suetake
  • Eiji Uchino
Regular Paper
  • 94 Downloads

Abstract

In the present paper, we propose an image contrast enhancement method that can enhance the contrast of a color image naturally by taking account of a color space shape. The proposed method realizes the natural enhancement based on two kinds of intensity histograms: a gradient-norm-based histogram and an ideal histogram derived from the shape of a color space. The former histogram is used to suppress over-enhancement in the flat regions of an image and the latter histogram is used to prevent the whole image from being darken. Concretely, the aforementioned intensity histograms are appropriately mixed into a histogram with a weight based on the average intensity of the input image. The contrast enhancement of the input image is realized using the cumulative histogram of the mixed histogram as an intensity transform function. To verify the validity of the proposed method, in experiments, the proposed method is applied to a variety of images and experimental results are evaluated qualitatively and quantitatively.

Keywords

Histogram equalization Histogram specification Contrast enhancement Gradient norm 

References

  1. 1.
    Rafael Gonzales, C., Richard Woods, E.: Digital Image Processing, 2nd ed. Prentice Hall, Upper Saddle River (2002)Google Scholar
  2. 2.
    Pizer, S.M., Philip Ambum, E., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vision Graph. Image Process. 39(3), 355–368 (1987)CrossRefGoogle Scholar
  3. 3.
    Mlsna, P.A., Rodriguez, J.J.: A multivariate contrast enhancement technique for multispectral images. IEEE Trans. Geosci. Remote Sens. 33(1), 212–216 (1995)ADSCrossRefGoogle Scholar
  4. 4.
    Mlsna, P.A., Zhang, Q., Rodriguez, J.J.: 3-D histogram modification of color images. In: Proceedings of IEEE International Conference on Image Processing. 3, 1015–1018 (1996)Google Scholar
  5. 5.
    Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (1997)CrossRefGoogle Scholar
  6. 6.
    Wang, Y., Chen, Q., Zhang, B.: Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans. Consum. Electron. 45(1), 68–75 (1999)CrossRefGoogle Scholar
  7. 7.
    Zhu, H., Chan, F.H.Y., Lam, F.K.: Image contrast enhancement by constrained local histogram equalization. Comput. Vision Image Underst. 73(2), 281–290 (1999)CrossRefGoogle Scholar
  8. 8.
    Alex Stark, J.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9(5), 889–896 (2000)ADSCrossRefGoogle Scholar
  9. 9.
    Chen, S.D., Ramli, A.R.: Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans. Consum. Electron. 49(4), 1310–1319 (2003)CrossRefGoogle Scholar
  10. 10.
    Cheng, H.D., Shi, X.J.: A simple and effective histogram equalization approach to image enhancement. Digit. Signal Process. 14(2), 158–170 (2004)CrossRefGoogle Scholar
  11. 11.
    Chen, S.-D., Ramli, A.R.: Preserving brightness in histogram equalization based contrast enhancement techniques. Digit. Signal Process. 14(5), 413–428 (2004)CrossRefGoogle Scholar
  12. 12.
    Abdullah-Al-Wadud, M., Kabir, M.H., Ali Akber Dewan, M., Chae, O.: A dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53(2), 593–600 (2007)CrossRefGoogle Scholar
  13. 13.
    Sim, K.S., Tso, C.P., Tan, Y.Y.: Recursive sub-image histogram equalization applied to gray scale images. Pattern Recognit. Lett. 28(10), 1209–1221 (2007)CrossRefGoogle Scholar
  14. 14.
    Ooi, C.H., Kong, N.S.P., Ibrahim, H.: Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Trans. Consum. Electron. 55(4), 2072–2080 (2009)CrossRefGoogle Scholar
  15. 15.
    Inoue, K., Hara, K., Urahama, K.: Gradient norm-based histogram equalization. J. Inst. Image Inf. Telev. Eng. 67(8), J296–299 (2013). (in Japanese)Google Scholar
  16. 16.
    Naik, S.K., Murthy, C.A.: Hue-preserving color image enhancement without gamut problem. IEEE Trans. Image Process. 12(12), 1591–1598 (2003)ADSCrossRefGoogle Scholar
  17. 17.
    Murahira,K., Taguchi, A.: Color image enhancement based on histogram equalization technique. IEICE Trans. Fund. Electron. Commun. Comput. Sci. J95-A(12), 817–821 (2012). (in Japanese)Google Scholar
  18. 18.
    Murahira, K., Kawakami, T., Taguchi, A.: Histogram equalization with variable enhancement degree. IEEJ Trans. Electron. Inf. Syst. 130(1), 158–159 (2010). (in Japanese)Google Scholar

Copyright information

© The Optical Society of Japan 2017

Authors and Affiliations

  • Yoshiaki Ueda
    • 1
  • Takanori Koga
    • 2
  • Hideaki Misawa
    • 3
  • Noriaki Suetake
    • 1
  • Eiji Uchino
    • 1
  1. 1.Yamaguchi UniversityYamaguchiJapan
  2. 2.National Institute of TechnologyTokuyama CollegeShunanJapan
  3. 3.National Institute of TechnologyUbe CollegeUbeJapan

Personalised recommendations