Salt and Pepper Noise: Effects and Removal
DOI: http://dx.doi.org/10.30630/joiv.2.4.151
Abstract
Noises degrade image quality which causes information losing and unsatisfying visual effects. Salt and Pepper noise is one of the most popular noises that affect image quality. In RGB color image Salt and pepper noise changes the number of occurrences of colors combination depending on the noise ratio. Many methods have been proposed to eliminate Salt and Pepper noise from color image with minimum loss of information. In this paper we will investigate the effects of adding salt and pepper noise to RGB color image, the experimental noise ratio will be calculated and the color combination with maximum and minimum numbers of occurrence will be calculated and detected in RGB color image. In addition this paper proposed a methodology of salt and pepper noise elimination for color images using median filter providing the reconstruction of an image in order to accept result with minimum loss of information. The proposed methodology is to be implemented, tested and experimental results will be analyzed using the calculated values of RMSE and PSNR.
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Majed O. Al-Dwairi, Ziad A. Alqadi, Amjad A. AbuJazar and Rushdi Abu Zneit, Optimized True-Color Image Processing, World Applied Sciences Journal 8 (10): 1175-1182, 2010 ISSN 1818-4952 © IDOSI Publications, 2010.
Zahran B., Jamil Al-Azzeh , Alqadi Z., Alzoghoul M. Khawatreh M, A Modified LBP Method To Extract Features From Color Images, Journal of Theoretical and Applied Information Technology; May 2018., vol. 96 Issue 10, p3014.
Akram A. Moustafa and Ziad A. Alqadi, Color Image Reconstruction Using A New R'G'I Model, Journal of Computer Science 5 (4): 250-254, 2009 ISSN 1549-3636 © 2009 Science Publications.
Firas Ajil Jassim, Kriging Interpolation Filter to Reduce High Density Salt and Pepper Noise, World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 3, No. 1, 8-14, 2013.
D. Puig and M. Angel García,“Determiningoptimal window size for texture feature extraction methods”, IX Spanish Symposium on Pattern Recognition and Image Analysis, Castellon, Spain, May, vol. 2, pp. 237-242, 2001.
E. H. Issaks, and R. M. Srivastava, An Introduction to Applied Geostatistics. Oxford: Oxford University Press, 1989.
F. A. Jassim, “Image Denoising Using Interquartile Range Filter with Local Averaging”, International Journal of Soft Computing and Engineering (IJSCE), vol. 2, Issue 6, pp: 424 – 428, January 2013.
K. Vasanth, S. Karthik, and S. Divakaran, “RemovalofSalt&Pepper Noise using Unsymmetrical Trimmed Variants as Detector”, European Journal of Scientific Research, vol. 70, no.3, pp. 468-478, 2012.
M. R. Stytz and R. W. Parrott. Using kriging for 3d medical imaging. Computerized Medical Image., Graphics, vol. 17, no. 6, pp.421–442, 1993.
N.Alajlan,M.Kamel,E.Jernigan,“DetailPreservingimpulsenoise removal”, International journal on Signal processing: image communication, vol 19, pp. 993-1003, 2004.
T. Vimala,“SaltAndPepperNoiseReductionUsing MdbutmFilter WithFuzzyBasedRefinement”,Volume 2, Issue 5, May 2012.
W. C. M. Van Beers and J. P. C. Kleijnen “Kriging for interpolation in random simulation”,Journal of the Operational Research Society , vol. 54, pp. 255–262, 2003.
W. K. Pratt, Digital Image Processing, Fourth Edition, John Wiley & Sons, Inc., Publication, 2007.
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is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.