Indonesian Journal of Electrical Engineering and Computer Science Vol. 27, No. 3, September 2022, pp. 1502~1508 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v27.i3.pp1502-1508  1502 Journal homepage: http://ijeecs.iaescore.com Human visualization system based intensive contrast improvement of the collected COVID-19 images Bashra Kadhim Oleiwi 1 , Layla H. Abood 1 , Maad Issa Al Tameemi 2 1 Department of Control and Systems Engineering, University of Technology-Iraq, Baghdad, Iraq 2 Department of Computer Engineering, University of Baghdad, Baghdad, Iraq Article Info ABSTRACT Article history: Received Sep 19, 2021 Revised May 28, 2022 Accepted Jun 10, 2022 Enhancement and color correction of images play an important role and can be considered as one of the fundamental and basic operations in image analysis for the purpose of speeding up the diagnosis of the medical images. Improving the quality and contrast of the medical image is the basic requirement for clinicians for obtaining an accurate and accurate medical diagnosis. Thus, getting a clear X-ray image reduces the effort and time- wasting. In this study a new idea will be applied for improving image contrast of the collected COVID-19 X-ray images, this idea is based on using Wiener filter, multilevel of histogram equalization (HE) technique with OpenCV library and then using contrast limited adaptive histogram equalization (CLAHE) techniques with OpenCV library. The proposed methodology programmed in MATLAB software and then implemented using Rasperry Pi 3 model B. The size and resolution of images are different as inputted images and this difference succeeded in proving the strength of the proposed idea. The collected X-ray images have undergone experiential evaluations which clearly showed the effective performance of the proposed methodology. Keywords: CLAHE technique COVID-19 chest X-rays images HE technique Image contrast enhancement OpenCV Wiener filter This is an open access article under the CC BY-SA license. Corresponding Author: Bashra Kadhim Oleiwi Department of Control and Systems Engineering, University of Technology-Iraq Industry Street, Baghdad, Iraq Email: bushra.k.oleiwi@uotechnology.edu.iq 1. INTRODUCTION Nowadays with the rapid developments of medicals images it has become very popular to obtain the definitive diagnosis of different medical cases. The contrast of the computed image must be enhanced by a set of techniques applied to improve image quality. Human eyes can’t detect the diagnosis of some medical image easily. Some images are not always functional without required contrast enhancement. Medical image enhancement now playing an important rule not only in the detection of diseases but also required in the urgent surgical cases, pregnancy, tracking serious, and early diagnosis. [1]. There are also some systems that use low-cost Raspberry Pi microcontrollers to control a system, detect, tracking, objects in image [2], [3] or even in medical field for diagnosis of infections like COVID-19 computerized tomography (CT) scans and chest X-ray images [4]. X-ray images that have been advanced in medical research are the main types of medical image. X-ray beam pass more quickly in skin than in bone this led to create a lighter area around the photographic plan’s bon like structure. X-ray picture is essential in many of applications in medicine. The most familiar use of X-rays is checking for fractures (broken bone), some tumors (breast cancer), and chest X-ray to diagnose infection (COVID-19) [5]. Contrast enhancement technique makes the image features stand out more clearly. Morphological operators have been used beside contrast enhancement technique to make important bone segments and soft tissues appear more clearly [1], [6]–[9]. Top-hat and bottom-hat