Ghofran Al-Badrani

Work place: Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Mosul, Nineveh, Iraq



Research Interests: Multimedia Information System, Computing Platform, Image Processing, Computer Vision, Computer systems and computational processes


Ghofran Al-Badrani was born in 1995. She received her BSc degree in Computer Science from the Department of Computer Science, University of Mosul in 2018. Currently, she is focusing her research on digital image processing, computer vision, multimedia technology in computing and soft computing.

Author Articles
Improving the Sharpness of Digital Image Using an Amended Unsharp Mask Filter

By Zohair Al-Ameen Alaa Muttar Ghofran Al-Badrani

DOI:, Pub. Date: 8 Mar. 2019

Many of the existing imaging systems produce images with blurry appearance due to various existing limitations. Thus, a proper sharpening technique is usually used to increase the acutance of the obtained images. The unsharp mask filter is a well-known sharpening technique that is used to recover acceptable quality results from their blurry counterparts. However, this filter often introduces an overshoot effect, which is an undesirable effect that makes the recovered edges appear with visible white shades around them. In this article, an amended unsharp mask filter is developed to sharpen different digital images without introducing the overshoot effect. In the developed filter, the image is smoothed by using the traditional bilateral filter and then blurred using a modified Butterworth filter instead of blurring it with a Gaussian low-pass filter only as in the traditional unsharp mask filter. Using this approach allowed to eliminate the overshoot effect and to recover better quality results. The proposed filter is assessed by using two modern image quality assessment metrics, real and synthetic-blurred images, and is compared with three renowned image sharpening techniques. Various experiments and comparisons showed that the proposed filter produced promising results with both real and synthetic-blurred images.

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