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Jayasri, S.
- Adaptive Mean Deviation Based Trimmed Median Filter for the Removal of High Density Salt and Pepper Noise
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Authors
Affiliations
1 Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, IN
2 Nano Electronics and Integration Division, IRRD Automatons, IN
1 Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, IN
2 Nano Electronics and Integration Division, IRRD Automatons, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 2 (2019), Pagination: 2109-2112Abstract
This paper suggests an effective algorithm based on the average variance from the digital images to eliminate salt and pepper noise. The proposed algorithm chooses the 33 window to unsymmetrically decorate the corrupted pixel and to swap the corrupted pixel for the median of the other pixel. In the chosen pane, on the other side, the screen width will be decreased by two when the whole pixel comprises 0 and 255 and the same process will be replicated. When noisy pixel values cannot even be achieved in a 7 × 7 frame, then the main pixel will be substituted with a small statistical variance. Experimental results show that the algorithm suggested continuously operates to reduce noise from salt and pepper. The unbiased, analytical analysis of the proposed algorithm shows that the proposed algorithm beats the current state-of-the-art algorithm for noise reduction, such as SMF, AMF, DBA and MDBUTMF.Keywords
Adaptive Median Filter, Decision Based Algorithm, Mean Deviation and Standard Median Filter.
References
- J. Astola and P. Kuosmaneen, “Fundamentals of Nonlinear Digital Filtering”, CRC Press, 1997
- J.B. Bednar and T.L. Watt, “Alpha-Trimmed means and their Relationship to Median Filter”, IEEE Transactions on Acoustics, Speech, Signal Processing, Vol. 32, No. 1, pp. 145-153, 1984.
- S. Esakkirajan, T. Veerakumar, Adabala N. Subramanyam and C.H. PremChand, “Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter”, IEEE Signal Processing Letters, Vol. 18, No. 5, pp. 287-290, 2011.
- R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, Prentice-Hall, 2002.
- Haidi Ibrahim and Nicholas Sia Pik Kong, “Simple Adaptive Median Filter for the Removal of Impulse Noise from Highly Corrupted Images” , IEEE Signal Processing Letters, Vol. 54, No. 4, pp. 1920-1927, 2008.
- H. Hwang and R.A. Hadded, “Adaptive Median Filters: New Algorithm and Results”, IEEE Transactions on Image Processing, Vol. 4, No. 4, pp. 499-502, 1995.
- S.J. Ko and Y.H. Lee, “Center-Weighted Median Filters and Their Applications to Image Enhancement”, IEEE Transactions on Circuits and Systems, Vol. 38, No. 9, pp. 984-992, 1991.
- W. Luo and D. Dang, “A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise”, IEEE Signal Processing Letters, Vol. 14, No. 3, pp. 193-196, 2007.
- W. Luo and D. Dang, “An Efficient Method for the Removal of Impulse Noise”, Proceedings of IEEE International Conference on Image Processing, pp. 2601-2604, 2006.
- Shuqun Zhang and Mohammad A. Karim, “A New Impulse Detector for Switching Median Filters”, IEEE Signal Processing Letters, Vol. 9, No. 11, pp. 360-363, 2002.
- K.S. Srinivasan and D. Ebenezer, “A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises”, IEEE Signal Processing Letters, Vol. 14, No. 3, pp. 189-192, 2007.
- Stephen Gorard, “Revisiting A 90-Year-Old Debate: The Advantages of the Mean Deviation”, British Journal of Educational Studies, Vol. 53, No. 4, pp. 417-430, 2005.
- V.R. Vijayakumar, G. Santhanamari and D. Ebenezer, “Fast Switching Based Median-Mean Filter for High Density Salt and Pepper Noise Removal”, International Journal of Electronics and Communication, Vol. 68, No. 2, pp. 1145-1155, 2014.
- Z. Wang and D. Zhang, “Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images”, IEEE Transactions on Circuits System II: Analog and Digital Signal Processing, Vol. 46, No. 1, pp. 78-80, 1999.