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Home»Articles»Performance Analysis of Median Filter With Respect to Different Padding Methods in the Context of Removing Salt and Pepper Noise

Performance Analysis of Median Filter With Respect to Different Padding Methods in the Context of Removing Salt and Pepper Noise

Author : T. Sudha and P. Nagendra Kumar
Volume 8 No.3 Special Issue:June 2019 pp 133-136

Abstract

Image Processing is one of the major areas of research. Images are often corrupted with different types of noise such as Gaussian noise, Poisson noise, Salt and Pepper noise, Speckle noise etc.The present work analyses the performance of the median filter with respect to different padding methods in the context of removing salt and pepper noise.Peak Signal-to-Noise ratio and Mean Squared Error have been considered as parameters for performance evaluation. The results obtained show that the Peak Signal-to-Noise Ratio and Mean Squared Error obtained between the original image and the filtered image obtained by applying median filter with symmetric padding method on the image corrupted with salt and pepper noise is same as the Peak Signal-to-Noise Ratio and Mean Squared Error obtained between the original image and the filtered image obtained by applying median filter with replicate padding method on the image corrupted with salt and pepper noise respectively.

Keywords

Median Filter, Salt and Pepper Noise, Padding

Full Text:

References

[1] The Wikipedia website [Online] Available at: https://en. wikipedia.org/ wiki/Image_noise.html
[2] The Wikipedia website [Online] Available at: https://en.wikipedia. org/wiki/Salt-and- pepper_noise.html
[3] The Mathworks website [Online]. Available at: https://www.math works.com/help/images/what-is-image-Filtering-in-the-spatial-domain.html
[4] The Math works website [Online] Available at: https://www.math works.com/help/images/sliding-neigh borhood-operations.html
[5] The Wikipedia website [Online] Available at: https://www.wikipedia. org/wiki/Medianfilter.html
[6] The Dartmouth website [Online] Available at: http://www. dartmouth. edu/doc/id1/html/filtering an image.html
[7] Pranay Yadav, Prayag Tiwari and Vivek Kumar, “Comparative Performance Analysis of Image Denoising techniques, International Conference on Innovations in Engineering and Technology, Bangkok. Dec. 25-26, 2013
[8] R. Srinivas and Satapura Panda, “Performance Analysis of various filters for Image Noise Removal indifferent Noise Environment”, International Journal of Advanced Computer Research, Vol.3, No.4, No.13, December 2013.
[9] Bhawna and SukhjeetKaurRanade, “Performance Analysis of Filter Based Techniques for Image Denoising”, International Journal of Computer Science and Technology, Vol.6, No.2, April-June 2015
[10] RituPatil and PratibhaNagaich,” Performance Analysis of Image Denoising with wavelet transform and Median Filter over AWGN channel”,International Journal of Advanced Research in Electronics and Communication Engineering, Vol.4, No.10, October-2015
[11] SumanShrestha, “Image Denoising using New Adaptive Based Median Filter”, Signal & Image Processing: An International Journal (SIPIJ), Vol.5, No.4, August-2014.
[12] J. SelvaPriya and P. Senthil Kumar, “Performance analysis of Linear and Nonlinear Filters for Image Denoising”, International Journal of Advance Research in Engineering, Science and Technology, Vol. 5, No. 3, March 2018.
[13] Wang, Zhou and Zhang, “Progressive Switching Median Filter for the removal of Impulse noise from highly corrupted images”, IEEE transactions on circuits and Systems: Analog and Digital Signal Processing”, February 1999.
[14] Hongjin Ma and YufengNie, “A two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and Adaptive directional mean filter”, PLoS One, Vol. 13, No. 10, October 2018.
[15] K.K.V. Toh, H. Ibrahim and M.N. Mahyuddin, “Salt-and-Pepper noise detection and reduction using fuzzy switching median filter”, IEEE transactions on Consumer Electronics, Vol. 54, No.4, November 2008.
[16] Jayanta Das, Bhaswati Das, JesmineSaikia and S.R. Nirmala, “Removal of Salt and Pepper Noise using Selective Adaptive Median Filter”, International Conference on Accessibility to Digital World, 2010.

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Image Processing is one of the major areas of research. Images are often corrupted with different types of noise such as Gaussian noise, Poisson noise, Salt and Pepper noise, Speckle noise etc.The present work analyses the performance of the median filter with respect to different padding methods in the context of removing salt and pepper noise.Peak Signal-to-Noise ratio and Mean Squared Error have been considered as parameters for performance evaluation. The results obtained show that the Peak Signal-to-Noise Ratio and Mean Squared Error obtained between the original image and the filtered image obtained by applying median filter with symmetric padding method on the image corrupted with salt and pepper noise is same as the Peak Signal-to-Noise Ratio and Mean Squared Error obtained between the original image and the filtered image obtained by applying median filter with replicate padding method on the image corrupted with salt and pepper noise respectively.

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Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
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Department of Computer Science, Basra University, Iraq
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Department of Information System, University of Malaya, Malaysia
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Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
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Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
h[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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