J. ICT Res. Appl., Vol. 14, No. 1, 2020, 95-114 95 Received December 12 th , 2019, 1 st Revision May 28 th , 2020, 2 nd Revision July 23 rd , 2020, Accepted for publication August 13 th , 2020. Copyright © 2020 Published by IRCS-ITB, ISSN: 2337-5787, DOI: 10.5614/itbj.ict.res.appl.2020.14.2.1 A Global Two-Stage Histogram Equalization Method for Gray-Level Images Khaled H Almotairi Computer Engineering Department, Umm Al-Qura University, 715, Alabidiah, Taif Street, Makkah, Saudi Arabia E-mail: khmotairi@uqu.edu.sa 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: gray-scale images; histogram equalization; image enhancement; image processing; images quality; visual appearance. 1 Introduction The current explosion of the creation and usage of digital images in our lives leads to the need for more digital image processing techniques to manipulate digital images using computers [1-3]. Digital images are created using several different methods ranging from mobile phone cameras for daily usage to satellite cameras for aerial images and from simple scanners for documents to medical scanners for the human body, and several more [3,4]. These different methods and conditions can lead to several undesirable effects, such as blurring, and degraded contrast and brightness as well as an incorrect balance of color levels [3-6]. Therefore, image enhancement techniques have attracted the attention of many researchers. Furthermore, image enhancement techniques aim to improve the quality of the visual appearance of images that suffer from the previously mentioned problems