Hue-preserving and saturation-improved color histogram equalization algorithm

Ki Sun Song, Hee Kang, Moon Gi Kang

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


In this paper, an algorithm is proposed to improve contrast and saturation without color degradation. The local histogram equalization (HE) method offers better performance than the global HE method, whereas the local HE method sometimes produces undesirable results due to the block-based processing. The proposed contrastenhancement (CE) algorithm reflects the characteristics of the global HE method in the local HE method to avoid the artifacts, while global and local contrasts are enhanced. There are two ways to apply the proposed CE algorithm to color images. One is luminance processing methods, and the other one is each channel processing methods. However, these ways incur excessive or reduced saturation and color degradation problems. The proposed algorithm solves these problems by using channel adaptive equalization and similarity of ratios between the channels. Experimental results show that the proposed algorithm enhances contrast and saturation while preserving the hue and producing better performance than existing methods in terms of objective evaluation metrics.

Original languageEnglish
Pages (from-to)1076-1088
Number of pages13
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number6
Publication statusPublished - 2016 Jun

Bibliographical note

Funding Information:
National Research Foundation of Korea (NRF); Ministry of Science, ICT and Future Planning (MSIP) (2015R1A2A1A14000912). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2015R1A2A1A14000912).

Publisher Copyright:
© 2016 Optical Society of America.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


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