Impulse Noise Reduction is very active research area in image processing. It is one of the important processes in the preprocessing of Digital Images. There are many techniques to remove the noise from the image and produce the clear visual of the image. Also there are several filters and image smoothing techniques available. All these available techniques have certain limitations. Recently, neural network are found to be very efficient tool for image Enhancement. In this, a two stage noise removal technique to deal with impulse noise is proposed. In the first stage, an additive two-level neural network is applied to remove the noise cleanly and keep the uncorrupted information well. In the second stage, the fuzzy decision rules inspired by human visual system are proposed to compensate the blur of the edge and destruction caused by median filter. An neural network is proposed to enhance the sensitive regions with higher visual quality.

1. Schulte, S., De Witte, V., Nachtegael, M., Van der Weken, D.and Kerre, E.E., "Fuzzy Two-Step Filter for Impulse Noise Reduction From Color Images", IEEE Transactions on Image Processing, Vol. 15, No. 11, Pp. 3567 – 3578, 2006
2. Sun Zhong-gui, Chen Jie and Meng Guang-wu, "An Impulse Noise Image Filter Using Fuzzy Sets", International Symposiums on Information Processing (ISIP), Pp. 183 – 186,2008.
3. Ibrahim, H., Kong, N.S.P. and Theam Foo Ng, "Simple adaptive median filter for the removal of impulse noise from highly corrupted images", IEEE Transactions on Consumer Electronics,Vol. 54, No. 4, Pp. 1920 - 1927, 2008.
4. Abreu, E., Lightstone, M., Mitra, S.K. and Arakawa, K., "A New Efficient Approach for the Removal of Impulse Noise from Highly Corrupted Images", IEEE Transaction on Image Processing, Vol. 5, No. 6, Pp. 1012-1025, 1996.
5. Russo, F. and Ramponi, G., "A Fuzzy Filter for Images Corrupted by Impulse Noise", IEEE Signal Processing Letters,Vol. 3, No. 6, Pp. 168-170, 1996.
6. Choi, Y.S. and Krishnapuram, R., "A Robust Approach to Image Enhancement Based on Fuzzy Logic", IEEE Transaction on Image Processing, Vol. 6, No. 6, Pp. 808-825, 1997.
7. Boskovitz, V. and Guterman, H., "An Adaptive NeuroFuzzy System for Automatic Image Segmentation and Edge Detection", IEEE Transactions on Fuzzy Systems, Vol. 10, No.2, Pp. 247-262, 2002.
8. T. Chen, K. K. Ma, and L. H. Chen, “Tri-state median filter for image de-noising,” IEEE Trans. Image Process., vol. 8, no. 12, pp. 1834–1838,Dec. 1999.
9. B. Azeddine, B. B. Kamel, and B. Abdelouahab, “Low-level vision treatments inspired from human visual system,” in Proc. 5th Int. Symp. Signal Process. Appl., ISSPA 999, Brisbane, Australia, Aug., pp. 313–316.
10. W. Luo, “An efficient detail-preserving approach for removing impulse noise in images,” IEEE Signal Process. Lett., vol. 13, no. 7, pp. 413–416,Jul. 2006.
11. P. E. Ng and K. K. Ma, “A switching median filter with boundary discriminative noise detection for extremely corrupted images,” IEEE Trans.Image Process., vol. 15, no. 6, pp. 1506– 1516, Jun. 2006
12. M. E. Y¨uksel, “A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise,” IEEE Trans. Image Process.,vol. 15, no. 4, pp. 928–936, Apr. 2006
13. J. Johnston, N. Jayant, and R. Safranek, “Signal compression based on models of human perception,” in Proc. IEEE, Oct. 1993, vol. 81, no. 10,pp. 1325–1422.
14. V.Saradhadevi and Dr.V.Sundaram,” A Novel Two-Stage Impulse Noise Removal Technique based on Neural Networks and Fuzzy Decision”, International Journal of Computer Applications, (0975 – 8887) Volume 21– No.3, May 2011
15. Sheng-Fu Liang, Shih-Mao Lu, Jyh-Yeong Chang, , and Chin-Teng (CT) Lin,” A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision”, IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 16, NO. 4, AUGUST 2008

Impulse noise Image enhancement neural network fuzzy decision rules