Fuzzy-Based Histogram Partitioning for Bi-Histogram Equalisation of Low Contrast Images

Mohammad Farhan Khan*, Deepali Goyal, Muaffaq M. Nofal, Ekram Khan, Rami Al-Hmouz, Enrique Herrera-Viedma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


The conventional histogram equalisation (CHE), though being simple and widely used technique for contrast enhancement, but fails to preserve the mean brightness and natural appearance of images. Most of the improved histogram equalisation (HE) methods give better performance in terms of one or two metrics and sacrifice their performance in terms of other metrics. In this paper, a novel fuzzy based bi-HE method is proposed which equalises low contrast images optimally in terms of all considered metrics. The novelty of the proposed method lies in selection of fuzzy threshold value using level-snip technique which is then used to partition the histogram into segments. The segmented sub-histograms, like other bi-HE methods, are equalised independently and are combined together. Simulation results show that for wide-range of test images, the proposed method improves the contrast while preserving other characteristics and provides good trade-off among all the considered performance metrics.

Original languageEnglish
Article number8954713
Pages (from-to)11595-11614
Number of pages20
JournalIEEE Access
Publication statusPublished - 2020


  • Contrast enhancement
  • dynamic range
  • fuzzy membership function
  • histogram equalisation
  • image transformation
  • optimal threshold

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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