Random Valued Impulse Noise Removal Using Region Based Detection Approach

S. Banerjee, A. Bandyopadhyay, A. Mukherjee, A. Das, R. Bag


Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.


random valued inpulse noise; image filtering; region based; detection

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S. Bandyopadhyay, S. Banerjee, A. Das, R. Bag, “A Relook and Renovation over State-of- Art Salt and Pepper Noise Removal Techniques”, I.J. Image, Graphics and Signal Processing, Vol. 9, pp. 61-69, 2015

Y. Dong, R. H. Chan, S. Xu, “A detection statistic for random valued impulse noise”, IEEE Transactions on Image Processing Vol. 16, No. 4, pp. 1112-1120, 2007

J. W. Tukey, Exploratory Data Analysis, Reading, Addision-Wesley, 1971

G. R. Arce, R. E. Foster, “Detail-preserving ranked-order based filters for image processing”, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 37, No. 1, pp. 83–98, 1987

W. Y. Han, J. C. Lin, “Minimum-maximum exclusive mean (MMEM) filter to remove impulse noise from highly corrupted images”, Electronics Letters, Vol. 33, No. 2, pp. 124–125, 1997

Y. H. Lee, S. A. Kassam, “Generalized median filtering and related nonlinear filtering techniques”, IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 33, No. 3, pp. 672–683, 1985

E. Abreu, M. Lightstone, S. K. Mitra, K. Arakawa, “A New Efficient Approach for the Removal of Impulse Noises from Highly Corrupted Images”, IEEE Transactions on Image Processing, Vol. 5, No. 6, pp.1012-1025, 1996

T. Chen, K. K. Ma, H. L. Chen, “Tri-state median-based filters in image de-noising”, IEEE Transactions on Image Processing Vol. 8, No. 12, pp. 1834–1838, 1999

T. Chen, H R Wu, “Adaptive impulse detection using center weighted median filters”, IEEE Signal Processing Letters, Vol. 8, No. 1, pp. 1–3, 2001

W. Luo, “An efficient algorithm for the removal of impulse noise from corrupted images AEU - International Journal of Electronics and Communications, Vol. 61, No. 8, pp. 551–555, 2007

N. I. Petrovic, V. Crnojevic, “Universal impulse noise filter based on genetic programming”, IEEE Transactions on Image Processing, Vol. 17, No. 7, pp. 1109–1120, 2008

J. Wu, C. Tang, “PDE-Based Random-Valued Impulse Noise Removal Based on New Class of Controlling Functions”, IEEE Transactions on Image Processing, Vol. 20, No. 9, pp. 2428-2438, 2011

B. Xiong, Z. Yin, “A Universal de-noising framework with a new impulse detector and nonlinear means”, IEEE Transactions on Image Processing, Vol. 21, No. 4, pp. 1663-1675, 2012

K. Prathiba, R. Rathi, C. S. Christopher, “Random Valued Impulse Denoising using Robust Direction based Detection”, IEEE Conference on Information and Communication Technologies, pp. 1237-1242, 2013

Sarkar, S. Changder, J. K. Mandal, “A Threshold based Directional Weighted Median Filter for Removal of Random Impulses in Thermal Images”, IEEE 2nd International Conference on Business and Information Management, pp. 69-74, 2014

S. Banerjee, A. Bandyopadhyay, R. Bag, A. Das, “A Deviation Based Identification of Random Valued Impulse Noise Towards Image Filtering Using Neighborhood Approximation”, 3rd International Conference, Foundations and Frontiers in Computer, Communication and Electrical Engineering, pp. 217-220, 2016

S. Banerjee, A. Bandyopadhyay, R. Bag, A. Das, “Sequentially Combined Mean-Median Filter for High Density Salt and Pepper Noise Removal”, IEEE International Conference on Research in Computational Intelligence and Communication Networks, pp. 21-26, 2015

Z. Wang, C. A. Bovik, R. H. Sheikh, P. E. Simoncelli, “Image quality assessment from error visibility to structural similarity”, IEEE Transactions on Image Processing, Vol. 4 No. 13, pp. 600-612, 2004

Joshi, A. K. Boyat, B. K. Joshi, “Impact of Wavelet Transform and Median Filtering on Removal of Salt and Pepper Noise in Digital Images”, International Conference on Issues and Challenges in Intelligent Computing Techniques, pp. 838-843, 2014

S. Banerjee, A. Bandyopadhyay, R. Bag, A. Das, “Moderate Density Salt & Pepper Noise Removal”, International Journal of Electronics & Communication Technology, Vol. 6, No. 1, pp. 44-48, 2015

S. Banerjee, A. Bandyopadhyay, R. Bag, A. Das, “Neighborhood Based Pixel Approximation for High Level Salt and Pepper Noise Removal”, CiiT International Journal of Digital Image Processing, Vol. 6, No. 8, pp. 346-351, 2014

S. Banerjee, A. Taraphdar, R. Bag, A. Das, “Binary expansion based de-noising algorithm for an image corrupted by Gaussian noise”, CSI Transaction on ICT, Springer, pp. 1-5, 2016

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