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A Global Two-Stage Histogram Equalization Method for Gray-Level Images

Journal of ICT Research and Applications (JICTRA) · Vol. 14 No. 2 (2020) · pp. 95–114

DOI
10.5614/10.5614/itbj.ict.res.appl.2020.14.2.1
ISSN
2338-5499, 2337-5787
Published
2020-10-31
Publisher
DRI - ITB
Language
eng

Abstract

Digital image histogram equalization is an important technique in image processing to improve the quality of the visual appearance of images. However, the available methods suffer from several problems such as side effects and noise, brightness and contrast problems, loss of information and details, and failure in enhancement and in achieving the desired results. Therefore, the Adaptive Global Two-Stage Histogram Equalization (GTSHE) method for visual property enhancement of gray-level images is proposed. The first stage aims to clip the histogram and equalize the clipped histogram based on the number of occurrences of gray-level values. The second stage adaptively adjusts the space between occurrences by using a probability density function and different cumulative distribution functions that depend on the available and missing gray-level occurrences. Experiments were conducted using a number of benchmark datasets of images such as the Galaxies, Biomedical, Miscellaneous, Aerials, and Texture datasets. The results of the experiments were compared with a number of well-known methods, i.e. HE, AHEA, ESIHE, and MVSIHE, to evaluate the performance of the proposed method. The evaluation analysis showed that the proposed GTSHE method achieved a higher accuracy rate compared to the other methods.

Keywords

Collaboration network of the authors

2 authors · 1 co-authorship link · 1 across different organisations

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  • Umm al-Qura University
  • Taif University
  • Different organisations
  • Same organisation
  • Affiliation unknown
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Co-authorOrganisationJoint articles
Azhar Abbad AlsufyaniTaif University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A Global Two-Stage Histogram Equalization Method for Gray-Level Images</dc:title>
	<dc:creator>Almotairi, Khaled</dc:creator>
	<dc:subject xml:lang="en-US">gray-scale images</dc:subject>
	<dc:subject xml:lang="en-US">histogram equalization</dc:subject>
	<dc:subject xml:lang="en-US">image enhancement</dc:subject>
	<dc:subject xml:lang="en-US">image processing</dc:subject>
	<dc:subject xml:lang="en-US">images quality</dc:subject>
	<dc:subject xml:lang="en-US">visual appearance</dc:subject>
	<dc:description xml:lang="en-US">Digital image histogram equalization is an important technique in image processing to improve the quality of the visual appearance of images. However, the available methods suffer from several problems such as side effects and noise, brightness and contrast problems, loss of information and details, and failure in enhancement and in achieving the desired results. Therefore, the Adaptive Global Two-Stage Histogram Equalization (GTSHE) method for visual property enhancement of gray-level images is proposed. The first stage aims to clip the histogram and equalize the clipped histogram based on the number of occurrences of gray-level values. The second stage adaptively adjusts the space between occurrences by using a probability density function and different cumulative distribution functions that depend on the available and missing gray-level occurrences. Experiments were conducted using a number of benchmark datasets of images such as the Galaxies, Biomedical, Miscellaneous, Aerials, and Texture datasets. The results of the experiments were compared with a number of well-known methods, i.e. HE, AHEA, ESIHE, and MVSIHE, to evaluate the performance of the proposed method. The evaluation analysis showed that the proposed GTSHE method achieved a higher accuracy rate compared to the other methods.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2020-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/12836</dc:identifier>
	<dc:identifier>10.5614/10.5614/itbj.ict.res.appl.2020.14.2.1</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 14 No. 2 (2020); 95-114</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/12836/4971</dc:relation>
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