An Expeditious Deblurring for Computed Tomography Medical Images Using a Gaussian Prior Deconvolution
Department of Information Technology, Lebanese French University, Erbil, Kurdistan Region, Iraq
- *Corresponding Author:
- Al-Ameen Z
Department of Information Technology
Lebanese French University
Erbil, Kurdistan Region, Iraq
Tel: 00964750441 2721
E-mail: [email protected]
Received date: November 06, 2015; Accepted date: January 08, 2016; Published date: January 18, 2016
Citation: Al-Ameen Z (2016) An Expeditious Deblurring for Computed Tomography Medical Images Using a Gaussian Prior Deconvolution. J Tomogr Simul 1:103.
Copyright: © 2016 Al-Ameen Z. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The blurring artifact may affect computed tomography (CT) images due to various real-world limitations. Such prevalentdegradation is usually difficult to avert and it highly contributes in concealing important medical information that already exists in an image. As a consequence, the visual quality of the recorded images is reduced tremendously. Thence, different deblurring concepts have been introduced to address this ill-posed problem. The drawbacks with many of the contemporarydeblurring methods are high complexity and processing times. Hence, a Gaussian prior deconvolution is adopted in this study because of its ability to provide an efficient and fast processing which is convenient for CT images. Intensive tests have been conducted to attest the validity of this algorithm, for which both naturally and synthetically degraded CT images are utilized. Furthermore, the quality of the synthetic data is measured
using two advanced image quality metrics of feature similarity and structural similarity. The results obtained from the conducted experiments and their related performance assessments revealed the effectiveness and favorability of the adopted algorithm, in that it outperformed many famous algorithms in terms of recorded accuracy, speed and visual quality.