The International Arab Journal of Information Technology (IAJIT)

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Generalization of Impulse Noise Removal Hussain Dawood1, Hassan Dawood2, and Ping Guo3 1Faculty of Computing and Information Technology, University of Jeddah, Saudi Arabia 2Department of Software Engineering, University of Engineering and Technology, Pakistan 3Image Processing and Pattern Recognition Laboratory, Beijing Normal University, China

In this paper, a generalization for the identification and removal of an impulse noise is proposed. To remove the salt-and-pepper noise an Improved Directional Weighted Median Filter (IDWMF) is proposed. Number of optimal direction are proposed to increase from four to eight directions to preserve the edges and to identify the noise, effectively. Modified Switching Median Filter (MSMF) is proposed to replace the identified noisy pixel. In which, two special cases are considered to replace the identified noisy pixel. To remove the random-valued impulse noise, we have proposed an efficient random- valued impulse noise identifier and removal algorithm named as Local Noise Identifier and Multi-Texton Removal (LNI-MTR). We have proposed to use the local statistics of four neighbouring and the central pixel for the identification of noisy pixel in current sliding window. The pixel identified as noisy, is proposed to replace by using the information of multi-texton in current sliding window. Experimental results show that the proposed methods cannot only identify the impulse noise efficiently, but also can preserve the detailed information of an image.


[1] Aizenberg I. and Butakoff C., Effective Impulse Detector Based on Rank-Order Criteria, IEEE Signal Processing Letters, vol. 11, no. 3, pp. 363- 366, 2004.

[2] Akkoul S., Ledee R., Leconge R., and Harba R., A new adaptive switching median filter, IEEE Signal processing letters, vol. 17, no. 6, pp. 587- 590, 2010.

[3] Awad A. and Man H., High Performance Detection Filter for Impulse Noise Removal in Images, Electronics Letters, vol. 44, no. 3, pp. 192-194, 2008.

[4] Badri L., Development of Neural Networks for Noise Reduction, The International Arab Journal of Information Technology, vol. 7, no. 3, pp. 289-294, 2010.

[5] Bovik C., Handbook of image and video processing, Academic press, 2010.

[6] Caiquan J. and Dehua L., Adaptive Center- Weighted Median Filter, Journal of Huazhong University of Science and Technology (Nature Science Edition), vol. 8, 2008.

[7] Chen P. and Lien C., An Efficient Edge- Preserving Algorithm for Removal of Salt-and- Pepper Noise, IEEE Signal Processing Letters, vol. 15, pp. 833-836, 2008.

[8] Chen T. and Wu H.., Space Variant Median Filters for the Restoration of Impulse Noise Corrupted Images, IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 48, no. 8, pp. 784-789, 2001.

[9] Chen T., Ma K., and Chen L., Tri-State Median Filter for Image Denoising, IEEE Transactions on Image Processing, vol. 8, no. 12, pp. 1834- 1838, 1999.

[10] Crnojevic V., Senk V., and Trpovski Z., Advanced Impulse Detection based on Pixel- Wise MAD, IEEE Signal Processing Letters, vol. 11, no. 7, pp. 589-592, 2004.

[11] Dawood H., Dawood H., and Guo p., Removal of Random-Valued Impulse noise by Local Statistics. Multimedia Tools and Applications, vol. 74, no. 24, pp. 11485 11498, 2014.

[12] Dawood H., Dawood H., and Guo p., Removal of High-Intensity Impulse Noise by Weber s law Noise Identifier, Pattern Recognition Letters, 2014.

[13] Dawood H., Dawood H., and Guo p., Removal of Random-valued Impulse Noise by Khalimsky grid in Proceeding of Asia Pacific Conference on Multimedia and Broadcasting, Indonesia, pp. 82-87, 2015.

[14] Dong Y., and Xu S., 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.

[15] Esakkirajan S., Veerakumar T., Subramanyam A. and PremChand C., Removal of High Density Salt and Pepper Noise Through Modified Decision based Unsymmetric Trimmed Median Filter, Signal Processing Letters, vol. 18, no. 5, pp. 287-290, 2011.

[16] Hsieh M., Cheng F., Shie M., and Ruan S., Fast and Efficient Median Filter for Removing 199 Levels of Salt-and-Pepper Noise in Images, Engineering Applications of Artificial Intelligence, vol. 26, no. 4, pp. 1333-1338, 2012.

[17] Hwang H. and Haddad R., Adaptive Median Filters: New Algorithms and Results, IEEE Transactions on Image Processing, vol. 4, no. 4, pp. 499-502, 1995.

[18] Jin C., Yan M., and Jin S., An Approach to Remove Impulse Noise from a Corrupted Image, Journal of Optics, vol. 15, no. 2, pp. 025402, 2013.

[19] Ko S. and Lee Y., Center Weighted Median Filters and their Applications to Image Enhancement, IEEE Transactions on Circuits and Systems, vol. 38, no. 9, pp. 984-993, 1991. 706 The International Arab Journal of Information Technology, Volume 14, No. 5, September 2017

[20] Lu C. and Chou T., Denoising of Salt-and- Pepper Noise Corrupted Image using Modified Directional-Weighted-Median Filter, Pattern Recognition Letters, vol. 33, no. 10, pp. 1287- 1295, 2012.

[21] Mohapatra S., Sa P., and Majhi B., Adaptive Threshold Selection for Impulsive Noise Detection in Images using Coefficient of Variance, Neural Computing and Applications, vol. 21, no. 2, pp. 281-288, 2012.

[22] Pitas I., and Venetsanopoulos A., Order Statistics in Digital Image Processing, in Proceedings of the IEEE, Tucson, pp. 1893-1921, 1992.

[23] Sree P., Kumar P., Siddavatam R., and Verma R., Salt-and-Pepper Noise Removal by Adaptive Median-based Lifting Filter using Second- Generation Wavelets, Springer link , vol. 7, no. 1, pp. 111-118, 2013.

[24] Srinivasan K. and Ebenezer D., 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.

[25] Sun T. and Neuvo Y., Detail-Preserving Median based Filters in Image Processing, Pattern Recognition Letters, vol. 15, no. 4, pp. 341-347, 1994.

[26] Toh K. and Isa N., Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction, IEEE Signal Processing Letters, vol. 17, no. 3, pp. 281-284, 2010.

[27] Wan Y., Chen Q., and Yang Y., Robust Impulse Noise Variance Estimation based on Image Histogram, IEEE Signal Processing Letters, vol. 17, no. 5, pp. 485-488, 2010.

[28] Wang S. and Wu C., A New Impulse Detection and Filtering Method for Removal of Wide Range Impulse Noises, Pattern Recognition, vol. 42, no. 9, pp. 2194-2202, 2009.

[29] Windyga P., Fast Impulsive Noise Removal, IEEE Transactions on Image Processing, vol. 10, no. 1, pp. 173-179, 2001.

[30] Zhang S. and Karim M., A New Impulse Detector for Switching Median Filters, IEEE Signal Processing Letters, vol. 9, no. 11, pp. 360- 363, 2002.

[31] Zuo Z., Zhang T., Hu J., and Zhou G., A New Method for Removing Impulse Noise based on Noise Space Characteristic, Optik-International Journal for Light and Electron Optics, vol. 124, no. 18, pp. 3503-3509, 2013. Hussain Dawood received his MS and PhD degree in Computer Application Technology from Beijing Normal University, Beijing, China in 2012 and 2015, respectively. He is currently working as an Assistant Professor at Faculty of Computing and Information Technology, University of Jeddah, Jeddah, Saudi Arabia. His current research interests include image processing, pattern recognition, and feature extraction. Hassan Dawood is currently working as an Assistant Professor at Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan. His research interests include image restoration, feature extraction and image classification. He has received his MS and PhD degree in Computer Application Technology from Beijing Normal University, Beijing, china in 2012, and 2015, respectively. Ping Guo (IEEE Senior Member) is currently a professor at the Computer Science Department of Beijing Normal University, and the School of Computer Science and Technology of Beijing Institute of Technology. His current research interests include neural network, pattern recognition, image processing, software reliability engineering, optical computing, and spectra analysis.