Performance Analysis of Noise Removal Techniques For Digital Images

  • Wasan A. Alawsi Faculty of Science, University of Al-Qadisiyah, Iraq.
  • Zahraa Ch. Oleiwi Faculty of Science, University of Al-Qadisiyah, Iraq.
  • Ali H. Alwan University of Alkafeel, Iraq.
  • Maytham K. Fadhil Faculty of Science, University of Al-Qadisiyah, Iraq.
  • Hayder M. Hadi University of Al-Qadisiyah, Iraq.
  • Nagham K. Hadi College of Computer Science and Information Technology, University of Al-Qadisiyah, Iraq.
Keywords: noise removal;, image segmentation;, linear filter.

Abstract

Noise has impairment effect on image so it destroys its information. In order to overcome this problem, it is necessary to remove noise before extracting information and image processing as one of the preprocessing steps. A brief overview of various sources and types of image noise as well as noise removal technique of digital images has been produced in this paper. The general idea of this paper is to perform segmentation of the corrupted image before using noise removal filter. Noise removal filter has been used to eliminate noise for each segment of image individually after segmentation process. Then comparative study between noise removal filtering for whole image and segmentation based method has been produced with performance analysis in terms of MSE and PSNR. In this paper, MSE and PSNR are calculated for the resultant reconstructed image compared with the original image. Six image samples are used to test the Segmentation based noise removal approach. The segmentation based technique has shown best results in terms of MSE and PSNR in comparison with traditional noise removal method based filter.
Simulation result show that MSE is less and PSNR is high using segmentation based noise removal method especially for image which has more details, so it superior performance as compared to the traditional method.

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Published
2020-02-16
How to Cite
Alawsi, W., Oleiwi, Z., Alwan, A., Fadhil, M., Hadi, H., & Hadi, N. (2020). Performance Analysis of Noise Removal Techniques For Digital Images. Journal of Al-Qadisiyah for Computer Science and Mathematics, 12(1), Comp Page 15 -. https://doi.org/10.29304/jqcm.2020.12.1.659
Section
Computer article