An Image Denoising Algorithm Based on Modified Nonlinear Filtering
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Nonlinear filters have been widely used in noise reduction and smoothing of grayscale images. Nevertheless, these kinds of filters have limitation in image denoising because, while smoothing, in addition to the noise, they remove important features from the image. In this research, a new image denoising algorithm is proposed to retain important features and details while eliminating noise from the image. This algorithm is based on the use of nonlinear filters for smoothing grayscale images corrupted by random noise. The basic idea behind the proposed algorithm is to split noisy image into features and noise. The proposed method differs from ordinary nonlinear filters in the use of the idea of image noise reduction through iterative application of smoothing with successively different pixel neighborhood block sizes. Then, it calculates residuals between the original image and the smoothed versions with different pixel neighborhood sizes. The algorithm discards regions in the residuals that it ‘sees’ to contain noise by thresholding and some other cleaning operations. Then, a denoised image will be created by recombining the processed residual images with a selected smoothed version of the original image. Therefore, detect non-noise edges and features in the image, adapt and modify their behavior near the edges in order to preserve them. The proposed method outperformed the ordinary nonlinear filters by improving the subjective appearance of grayscale images corrupted by noises like Gaussian and in producing higher PSNR and lower MSE
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M.C Motwani, M.C. Gadiya and R.C. Motwani, Survey of Image Denoising Techniques. the Proceedings of the 2004 GSPx Conference, Santa Clara, CA. 2004.
M. Lindenbaum, M. Fischer, and A. Bruckstein, On Gabor Contribution to Image-Enhancement. Pattern Recognition, Vol. 27, pp. 1–8, 1994.
R. A. Peters II, A New Algorithm for Image Noise Reduction using Mathematical Morphology. IEEE Transactions on Image Processing, Vo. 4, n. 3, pp. 554-568, May 1995.
A. Buades, B. Coll and M. Morel, Image Denoising by Non-local Averaging, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp: 25-28, 2005
M. Mahmoudi and G. Sapiro, Fast Image and Video Denoising via Non-local Means of Similar Neighborhoods. IEEE Signal Processing Letters, Vol. 12, n. 12, 2005.
P. Perona and J. Malik, Scale Space and Edge Detection Using Anisotropic Diffusion, IEEE Trans. Patt. Anal. Mach. Intell., Vol. 12, pp. 629–639, 1990.
L. Alvarez, P-L Lions, and J-M Morel, Image Selective Smoothing and Edge Detection by Nonlinear Diffusion (II). SIAM Journal of numerical analysis, Vol. 29, pp. 845–866, 1992.
Yaroslavsky, L.P. and M. Eden, Fundamentals of Digital Optics (Birkhauser, 1996).
L. Rudin, and S. Osher. Nonlinear Total Variation Based Noise Removal Algorithms. Physica D, Vol. 60, pp. 259–268, 1992
R. R. Coifman and D. Donoho, Translation Invariant De-noising," Wavelets and Statistics, Springer Lecture Notes in Statistics, Vol. 103, 1995.
M. Kazubek, Wavelet Domain Image Denoising by Thresholding and Wiener Filtering, IEEE Signal Processing Letters, Vol. 10, pp. 324-326, 2003.
S. Kumar, P.M. Gupta, and A.K. Nagawat, Performance Comparison of Median and Wiener Filter in Image De-Noising. International Journal of Computer Applications, Vol. 12, n.4, 2010.
F. Jin, P. W. Fieguth, L. Winger, and E. Jernigan, Adaptive Wiener Filtering of Noisy Images and Image Sequences, In Proceedings of the 2003 International Conference on Image Processing, 2003.
G. Chang, S., B. Yu, and M. Vattereli, Adaptive Wavelet Thresholding for Image Denoising and Compression, IEEE Transactions of Image Processing, Vol. 9, pp. 1532-1546, 2000.
S. D. Ruikar and D. D. Doye, Image Denoising using Neighbors Variation with Wavelet, International Journal of Computer Applications. Vol. 42, n. 17, 2012.
A. Fathi and A. Reza Naghsh-Nilchi, Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function, IEEE Transactions on Image Processing, Vol. 21, n. 9, September 2012.
Y. Murali Mohan Babu, M.V. Subramanyam, and M.N. Giri Prasad, PCA based image denoising, Signal & Image Processing: An International Journal (SIPIJ), Vol.3, n.2, April 2012.
K. K. Lavania, Shivali and R. Kumar, Image Enhancement using Filtering Techniques, International Journal on computer Science and Engineering. Vol.4, n .1, pp.14-20, January 2012.
A. Bijalwan, A. Goyal, and N. Sethi, Wavelet Transform Based Image Denoise Using Threshold Approaches, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 1, n. 5, June 2012.
Alqadi, Z.A., Moustafa, A.A., Alduari, M., Zneit, R.A., True color image enhancement using morphological operations, (2009) International Review on Computers and Software (IRECOS), 4 (5), pp. 557-562.
Farahiah, N., Shahrizan, D., Ishak, S., Sarpinah, B., Jusoff, K., Fuzzy logic image enhancement, (2009) International Review on Computers and Software (IRECOS), 4 (4), pp. 440-446.
B. M. Kumar, R. V. Lavanya, Signal Denoising with Soft Threshold by using Chui-Lian (CL) Multiwavelet, International Journal of Electronics & Communication Technology, Vol. 2, n. 1, March 2011.
J. Ojanen and J. Heikkonen, A Soft Thresholding Approach For MDL Denoising, 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland, September 3-7, 2007.
Y. A. Al-Sbou, Artificial Neural Networks Evaluation as an Image Denoising Tool, World Applied Sciences Journal, Vol. 17, n. 2, pp. 218-227, 2012.
Sugitha, N., Arivazhagan, S., A new combined image denoising scheme for mixed noise reduction, (2013) International Review on Computers and Software (IRECOS), 8 (6), pp. 1407-1415.
Rajathi, G.M., Rangarajan, R., A new dual tree wavelet based image denoising using fuzzy shrink and lifting scheme, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 668-675.
Rajeshkannan, S., Korah, R., Improved stereo matching algorithm using contrast limited adaptive histogram equalization, (2013) International Review on Computers and Software (IRECOS), 8 (7), pp. 1549-1555.
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