Enhanced AMF & ACWMF Impulse Noise Removal Technique for Quantitative Measures of Signal Restoration of Image Quality
Volume 1, Issue 1, December 2016, Pages: 6-14
Received: Oct. 29, 2016;
Accepted: Nov. 19, 2016;
Published: Dec. 21, 2016
Views 2713 Downloads 98
Olanrewaju Ajanaku, Department of Physical Sciences, Ondo State University of Science and Technology, Okitipupa, Nigeria
D. O. Aborisade, Electronic & Electrical Engineering Department, Ladoke Akintola University of Science and Technology, Ogbomoso, Nigeria
G. A. Ibitola, Department of Physical Sciences, Ondo State University of Science and Technology, Okitipupa, Nigeria
Images are often corrupted by impulse noise due to noisy sensors or channel transmission errors. In removing impulse noise from the acquired images, various linear and nonlinear filtering methods have been employed by various researchers. These have the drawback of blurring fine details and destroying image edges during noise filtering. In order to overcome this limitation without compromising the useful information content of the digital image, an enhanced AMF and ACWMF impulse noise removal technique by the combination of Artificial Neural Network (ANN) and nonlinear filters. ANN with a back propagation training algorithm was employed at the first stage to detect the impulse noise from the acquired digital images. The detected impulse in the digital image was removed at the second stage of the filtering using Adaptive Centre Weight Median Filter (ACWMF) and Adaptive Median Filter (AMF). mean-square error (MSE), root mean-square error (RMSE) and the peak signal to noise ratio (PSNR) were used for performance evaluation with respect to the percentage of the noise in the corrupted image, and the result showed improvement both in quantitative measures of signal restoration and judgment of image quality.
D. O. Aborisade,
G. A. Ibitola,
Enhanced AMF & ACWMF Impulse Noise Removal Technique for Quantitative Measures of Signal Restoration of Image Quality, Engineering Science.
Vol. 1, No. 1,
2016, pp. 6-14.
Gonzalez R. C. and R. E. Woods, (2008) Digital Image Processing, Pearson Prentice Hall, Upper Saddle River, New Jersey, USA, 3rd Edition.
Pitas I. and Venetsanopoulos A. N. (1990), “Nonlinear Digital Filters: Principles and Applications”, Kluwer Academic Publishers.
Hadi Sadoghi Yazdi and Faranak Homayouni (2010) “Impulsive Noise Suppression of Images Using Adaptive Median Filter” International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 3, No. 3.
Umbaugh S. E. (1998), Computer Vision and Image Processing. A Practical Approach Using CVIP Tools, Prentice Hall.
Sun T. and Neuvo Y., (1994) “Detail-preserving median based filters in image Processing," Pattern Recognition Letters, vol. 15, pp. 341-347.
Bovik A. C., Huang T. S., and Munson D. C. (1985), “Edge-sensitive image restoration using order constrained least squares methods,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 33, pp. 1253–1263.
Astola J. and Kuosmanen P. (1997). “Fundamentals of Nonlinear Filtering”, Boca Raton CRC Press.
Aborisade D. O. (2011) “A Novel Fuzzy logic Based Impulse Noise Filtering Technique,” International Journal of Advanced Science and Technology Vol. 32, pp. 79-87.
Abreu E., Lightstone M., Mitra S. K., and Arakawa K. (1996), “A New Efficient Approach for the Removal of Impulse Noise from Highly Corrupted Images”, IEEE Transactions on Image Processing, 5 (6):1012–1025.
Chen T. and Wu H. R., (2001) “Space variant median filters for the restoration of impulse noise corrupted images,” IEEE Trans. on Circuits and Systems II: Analog and Digital Signal Processing, vol. 48, no. 8, pp. 784–789.
Butakoff C. and I. Aizenberg (2004). “Effective Impulse Detector Based on Rank-Order Criteria”, IEEE Signal Processing Letters, 11 (3): 363–366.
Jian-Feng Cai, Raymond H. Chan, Mila Nikolova, (2010) Fast Two-Phase Image Deblurring Under Impulse Noise, J Math Imaging Vis, Springer Science and Business Media, LLC, pp. 46–53.
Kulkarni R. K, Meher S., Nair J. M., (2010) An Adaptive Switching Filter for Removing Impulse Noise from Highly corrupted Images, ICGST-GVIP Journal, pp.4 53.
Ratna Babu K, Arun Rahul L, Vineet Souri P, Suneetha A,(2011)” Image Denoising in the presence of high level Salt and Pepper noise using Modified Median filter”, IJCST, Vol. 2, Sp 1, pp. 180-183.
Aalavandan J., Santhosh Baboo, (2012)” Enhanced Switching Median Filter for De-noising Ultrasound”, IJARCE, Vol. 3, No. 2, pp 363-367.
Abdul Majid, Choong-Hwan Lee, Muhammad, Tariq Mahmood, Tae-Sun Choi, (2012) Impulse noise filtering based on noise-free pixels using genetic programming, Knowl Inf Syst, Springer-Verlag, pp. 505–526.
Zayed M. Ramadan, Efficient Restoration Method for Images Corrupted with Impulse Noise,(2012) Circuits Syst Signal Process, Springer Science and Business Media, LLC, pp. 1397–1406.
Annadurai S and Shanmugalakshmi R (2009), “Fundamentals of Digital Image Processing”, Pearson Education.
Mohapatra Subrajeet (2008) ‘’Development of Impulsive Noise Detection Schemes for Selective Filtering in Images’’ National Institute of Technology Rourkela Rourkela–769 008, Orissa, India.
Haykin S. (1999), “Neural networks-A Compressive Foundation”, 2nd. ed., Prentice-Hall, Upper Saddle River, N. J.
Laiphrakpam Dolendro Singh and Khumanthem Manglem Singh, (2015)"Image Encryption using Elliptic Curve Cryptography"Eleventh International Multi-Conference on Information Processing, pp 472-481.
A. K. Bhandari, D. Kumar, A. Kumar and G. K. Singh, (2015)"Optimal sub-band adaptive thresholding based edge preserved satellite image denoising using adaptive differential evolution algorithm", Neurocomputing, vol. 174, pp. 698-721.