Zaman Awni Hasan

Work place: Department of General Education, College of Education and Languages, Lebanese French University, Erbil, Kurdistan Region, Iraq



Research Interests: Information Theory, Multimedia Information System, Image Processing, Computer Vision, Computational Learning Theory


Zaman Awni Hasan studied her Bachelor of Education (Information Technology) at the Department of General Education, Lebanese French University. She is majoring in information technology, specifically in digital image processing. She is interested in pursuing a career in the teaching field. Her current topics of interest include computer vision, digital image processing, educational technologies, educational information technology, educational techniques, e-learning, multimedia technology in education and research methodology.

Author Articles
A Low-Complexity Algorithm for Contrast Enhancement of Digital Images

By Zohair Al-Ameen Zaman Awni Hasan

DOI:, Pub. Date: 8 Feb. 2018

As known, the contrast is a highly important feature by which the visual quality of digital images can be judged as adequate or poor. Hence, many methods exist for contrast enhancement, where the complexity of those methods usually varies due to the utilization of different concepts. In this article, a simple yet efficient algorithm is introduced for contrast enhancement of digital images. The proposed algorithm consists of four distinct stages: In the first stage, the hyperbolic sine function is applied to provide a simple contrast modification. In the second stage, a modified power-law function is utilized to control the amount of contrast adjustment. In the third stage, the standard sigmoid function is used to remap the image pixels into an “S” shape, which can provide further contrast enhancement. In the final stage, a contrast stretching function is applied to remap the image pixels into their natural dynamic range. The performed computer experiments on different low-contrast images demonstrated the efficiency of the proposed algorithm in processing synthetic and real degraded images, as it provided better and clearer results when compared to several existing contrast enhancement algorithms. To end with, the proposed algorithm can be used as a contrast processing step in many image-related applications.

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