Computer Science & Engineering Department Maulana Azad National Institute of Technology, Bhopal, India
Images gets corrupted either during acquisition or transmission. Frequently occurring noise that might occur in images is impulse noise, because
of that various image processing operations such as image segmentation, object identification, and similarity matching etc. cannot be performed efficiently.
This paper focus on various existing image filtering techniques and their improvements. Several median-based denoising methods tends to work well for
low level impulse noise but perform poorly for high level impulse noise.
Aaditya Sharma,R. K. Pateriya."A Survey on various Image Filtering Approaches to remove Impulse Noise". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 05,pp.322-328, May - 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I510.pdf,
: Noise models, threshold based switching median filter, operator based median filter, morphological based median filter, statistics based
 A. K. Jain, Fundamentals of Digital image processing, Prentice Hall of India, ISBN 0-13-336165-9, 1989.
 R. Lukac and K. N. Plataninotis, Color Image Processing: Methods and Applications, CRC Press, 2007.
 R. C. Gonzalez and R .E .woods, Digital Image Processing, Pearson Education, 2009.
 S. Siltanen et al, â€œStatistical inversion for medical X-ray tomography with few radiographs I: General theoryâ€, Physics in Medicine and Biology, Vol. 48, pp. 1437-1463, 2003.
 S. Sridhar, Digital Image Processing, Oxford University Press 2011.
 E. Abreu et al,â€A new efficient approach for the removal of impulse noise from highly corrupted images, â€œIEEE Trans. Image Process., vol. 5, no. 6, pp.1012-1025, June 1996.
 A. Mishra and M. Shandilya, â€œFingerprint core point detection using gradient field maskâ€, International Journal of Computer Applications, vol. 2, no.8, pp.19-23, June 2010.
 M. Motwani, M. Gadiya, R. Motwani, and C. Frederick â€œA survey of image denoising techniquesâ€, Proc. Of GSPx 2004 ,Santa Clara Convention Center, Santa Clara ,CA ,pp. 1-7, Sept.2004.
 T.A .Nodes and N. C. Gallagher, Jr., â€œMedian filters: Some modifications and their propertiesâ€, IEEE Trans. Acoust., Speech , Signal Process., vol. ASSP -30 , pp. 739-746,May 1982.
 A. Mehrotra, K.K. singh, M.J. Nigam and K. Pal, â€œA novel algorithm for impulse noise removal and edge detectionâ€, International Journal of Computer Applications, vol.38, no. 7, pp.30-33, Jan.2012.
. U. Ghanekar, A.K. Singh and R. Pandey,â€ A new scheme for impulse detection in switching median filters for image filteringâ€, International conference on computational intelligence and multimedia Applications, ICCIMA07 , Sivakasi ,India,pp.442-446, Dec 2007.
. N. Krishnan and J. Varghese, R. K. Selvakumar et al., â€œSelective switching median filter for the removal of salt and pepper impulse noiseâ€, in proc. IEEE Third International Conference on wireless and optical Communications Networks WOCN 2006, Bangalore ,India,pp.1-5, April 2006.
 A.B.Hamza, P.L.Escamilla, J.M.Aroza, R. R. Roldan,â€ Removing noise and preserving details with relaxed median filtersâ€, Journal of Mathematical Imaging and Vision, vol.11,pp.161-177,1999.
 T. Chen, K.-K. Ma, and L.-H. Chen, â€œTri â€“state median filter for image denoisingâ€, IEEE Trans. Image Processing, vol.8, no. 12, pp. 1834-1838, Dec.1999.
 T. Chen and H.R. Wu,â€Space variant median filters for the restoration of impulse noise corrupted imagesâ€, IEEE Trans. Circuits Syst. II, Vol.48, no. 8, pp. 784-789, Aug. 2001.
 Z. Wang and D.Zhang,â€ Progressive switching median filter for removal of impulse noise from highly corrupted images,â€ IEEE Trans. Circuits Syst. II, Vol. 46, no. 1, pp.78-80, 1999.
 S. Zhang and A. Karim,â€ A new impulse detector for switching median filters,â€ IEEE Signal Process. Lett. , vol. 9, no.11, pp. 360-363, Nov .2002.
 O Kao, â€œModification of the LULU operators for preservation of critical image details,â€ International Conference on Imaging Science, Systems and Technology, Las Vegas, pp. 1-7, 2001.
 Y. Dong and S. Xu,â€ A new Directional weighted median filter for removal of random valued impulse noise,â€ IEEE Signal Process. Lett. , Vol. 14, no. 3, pp. 193-196, March 2007.
 P.E. Ng, and K. K. Ma, â€œA switching median filter with boundary discriminative noise detection for extremely corrupted images,â€ IEEE Trans. Image Process., Vol.15, no. 6, pp. 1506-1516, June 2006.
 Manuel Blum, Robert W. Floyd, Vaughan Pratt, Ronald L. Rivest, Robert E. Tarjan,â€ Time bounds for selection,â€ Journal of Computer and System Sciences, Vol. 7, Issue 4, pp. 448-461, Aug 1973.
 A.S.Awad and H. Man, â€œHigh Performance Detection Filter for impulse noise removal in images,â€ Electronics Letters, Vol. 44, no. 3, Jan 2008.
 A. Fabijanska and D. Sankowski,â€ Noise adaptive switching median based-filter for impulse noise removal from extremely corrupted images,â€ IET Image Process., Vol. 5, issue 5, pp. 472-480, 2011.
 S. Indu and C. Ramesh,â€ A noise fading technique for images highly corrupted with impulse noise,â€ International Conference on Computing: Theory and Applications, ICCTAâ€™07, Kolkata, India, pp. 627-632, 2007.
 A.K. Tripathi, U. Ghanekar and S. Mukhopadhyay,â€ Switching median filter: advanced boundary discriminative noise detection algorithm,â€ IET Image Process., Vol. 5, Issue 7, pp. 598 -610, 2011.
 D. Z. Feng, Y. Z. Ping, and X. Y. Lun,â€ High probability impulse noise removing algorithm based on mathematical morphology,â€ IEEE Signal Process. Lett. , Vol. 14, no. 1, pp. 31-34, Jan. 2007.
 X. M. Zhang, Z.P. Yin and Y.L.Xiong,â€ Adaptive switching mean filter using conditional morphological noise detector,â€ IEEE Electron Lett. , Vol. 44, no. 06, March 2008.
 W.Luo,â€ An efficient algorithm for the removal of impulse noise from corrupted images,â€ IEEE Trans. On Consumer Electronics, Vol 61, Issue 8, pp. 551-553, Sept. 2007.
 J.-S. R. Jang, C. â€“T. Sun, E. Mizutani, Neuro â€“Fuzzy and Soft Computing, PHI 2002.
 R. Pandey and Ghanekar,â€ Fuzzy filtering algorithms for image processing: Performance evaluation of various approaches,â€proceedings of the International conference on Cognition and Recognition, PES college of Engg. Mandya, Karnataka, India, pp. 95-101, 2005.
 M.E. Yuksel,â€ A simple neuro-fuzzy method for improving the performance of impulse noise filters for digital images,â€ Int. J. Electron. Commun. (AEU), Vol. 59, Issue 8, pp. 463-472, 2006.
 M.E. Yuksel,â€ A hybrid neuro-fuzzy filter for edge preserving restoration of image corrupted by impulse noise,â€ IEEE Trans. Image Processing, Vol. 15, no. 4, pp. 928-936, April 2006.
 M.E. Yuksel, and E. Besdok,â€ A simple neuro fuzzy impulse detection for efficient blur reduction of impulse noise removal operators for digital image,â€ IEEE Trans. Fuzzy. Systems Vol. 12, no.6, pp. 854-856, Dec. 2004.
 Hsien- Hsin Chou et. al., â€œA sparsity-ranking edge â€“preservation filter for removal of high-density impulse noiseâ€, International Journals of Electronics and Communications (AEU), Vol. 68, Issue. 11, pages 1129-1135, Nov-2014.
 Hsien- Hsin Chou et.al, â€œA noise-ranking switching filter for images with general fixed-valued impulse noiseâ€, Signal Processing, Vol. 106, pages 198-208,Jan 2015.