Open AccessImage contrast enhancement using triple clipped dynamic histogram equalisation based on standard deviation
Author(s) -
Zarie Majid,
Parsayan Ali,
Hajghassem Hassan
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5395
Subject(s) - histogram , standard deviation , brightness , histogram matching , artificial intelligence , clipping (morphology) , pixel , entropy (arrow of time) , balanced histogram thresholding , pattern recognition (psychology) , computer science , histogram equalization , mathematics , dynamic range , computer vision , image (mathematics) , statistics , optics , linguistics , physics , philosophy , quantum mechanics
Many histogram equalisations‐based methods have been proposed to be applied in contrast enhancement. However, a few methods that can simultaneously create a natural enhancement in images with low, median, and high brightness ranges are suggested. Here, a robust contrast enhancement algorithm, which is called triple clipped dynamic histogram equalisation based on standard deviation (TCDHE‐SD), is proposed. In the proposed method, the histogram of the input image is partitioned into three parts with approximately the same number of pixels based on the standard deviation. Then, the process of histogram clipping is performed on each sub‐histogram. After that, all histograms will be mapped to a new dynamic range by means of applying some simple calculations, and finally, the sub‐histogram equalisation process will be performed independently. This method is proposed to achieve the multiple purposes of the maximum average information content (entropy), controlling the enhancement ratio and preserving reasonable brightness. It also provides a natural enhancement by generating clear images with maximum details. The performance assessment of the proposed method in terms of entropy as well as visual quality based on the mean opinion score (MOS) indicates a significant advantage of it over the previous ones.