Denoising of image using bilateral filtering in multiresolution

Alaa Abid Muslam Abid Ali, Mohammed Iqbal Dohan, Saif Khalid Musluh

Abstract


One of the very efficient and resource conservative image processing methodology is with the help of bilateral filters. This technique filters the image without the help of edge smoothing but it does employs spatial averaging in a non-linear way. The filtering technique discussed above is very much dependent on the parameters of its filters. A very slight change in filter parameter values effects the outputs and results in a most drastic manner. In this paper, the author has worked on two contributions. In the applications concerning image denoising, the author has contributed in study of the parameter selection of bilateral filters which are optimal in nature. The contribution number two is about extending the present work i.e. extension of the filters which are bilateral in nature. In this process, the bilateral filtering of images is applied to the lower frequency sub-bands which is also known as approximation sub-band. This sub-band is obtained by using the wavelet transformations. Hence, a new framework for image denoising will be created which will be combination of multiresolution bilateral filtering and wavelets transformation techniques. As a matter of fact, this combination is efficient in contradicting noise from an image.

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References


D L Donoho, I M Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika. 1994; 81(3): 425-455.

D L Donoho, I M Johnstone, G Kerkyacharian, D Picard. Wavelet shrinkage: Asymptopia?. J. Roy. Statist. Assoc. B. 1995; 57(2): 301-369.

S G Chang, B Yu, M Vetterli. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 2000; 9(9): 1532-1546.

J Portilla, V Strela, M J Wainwright, E P Simoncelli. Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Trans. Image Process. 2003; 12(11): 1338-1351.

A Pizurica, W Philips. Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising. IEEE Trans. Image Process. 2006; 15(3): 654-665.

L Sendur, I W Selesnick. Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans. Signal Process. 2002; 50(11): 2744-2756.

L Sendur, I W Selesnick. Bivariate shrinkage with local variance estimation. IEEE Signal Process. Lett. 2002: 9(12): 438-441.

F Luisier, T Blu, M Unser. A new sure approach to image denoising: Inter-scale orthonormal wavelet thresholding. IEEE Trans. Image Process. 2007; 16(3): 593-606.

S. Lyu, E P Simoncelli. Statistical modeling of images with fields of gaussian scale mixtures. In Advances in Neural Information Processing Systems 19. B. Schölkopf, J. Platt, T. Hoffman, Eds. Cambridge, MA: MIT Press, 2007: 945-952.

M Elad, M Aharon. Image denoising via learned dictionaries and sparse representation. Presented at the IEEE Computer Vision and Pattern Recognition. 2006.

M Elad, M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process., 2006; 15(12): 3736-3745.

J Mairal, M Elad, G Sapiro. Sparse representation for color image restoration. IEEE Trans. Image Process. 2008; 17(1): 53-69.

K Dabov, V Katkovnik, A Foi, K Egiazarian. Image denoising with block-matching and 3D filtering. Presented at the SPIE Electronic Imaging: Algorithms and Systems V. 2006.

K Dabov, A Foi, V Katkovnik, K Egiazarian. Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 2007; 16(8): 2080-2095.

K Hirakawa, T W Parks. Image denoising using total least squares. IEEE Trans. Image Process. 2006; 15(9): 2730-2742.

C Tomasi, R Manduchi. Bilateral filtering for gray and color images. in Proc. Int. Conf. Computer Vision, 1998: 839-846.

J S Lee. Digital image smoothing and the sigma filter. CVGIP: Graph. Models and Image Process. 1983: 24(2): 255-269.

L Yaroslavsky. Digital Picture Processing-An Introduction. New York: Springer Verlag. 1985.

S M Smith, J M Brady. Susan-A new approach to low level image processing. Int. J. Comput. Vis. 1997; 23: 45-78.

M Elad. On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process. 2002; 11(10): 1141-1151.

D Barash. A fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Trans. Pattern Anal. Mach. Intell., 2002; 24(6): 844-847.

A Buades, B Coll, J Morel. Neighborhood filters and PDE’s. Numer. Math. 2006; 105: 1-34.

N Sochen, R Kimmel, R Malladi. A general framework for low level vision. IEEE Trans. Image Process. 1998; 7(3): 310-318.

N Sochen, R Kimmel, A M Bruckstein. Diffusions and confusions in signal and image processing. J. Math. Imag. Vis., 2001; 14(3): 195-209.

A Spira, R Kimmel, N Sochen. A short time beltrami kernel for smoothing images and manifolds. IEEE Trans. Image Process. 2007; 16(6): 1628-1636.

C Kervrann, J Boulanger. Optimal spatial adaptation for patchbased image denoising. IEEE Trans. Image Process. 2006; 15(10): 2866-2878.

F Durand, J Dorsey. Fast bilateral filtering for the display of highdynamic-range images. In Proc. SIGGRAPH. 2002: 257-266.

E Eisemann, F Durand. Flash photography enhancement via intrinsic relighting. In Proc. SIGGRAPH. 2004: 673-678.

W C K Wong, A C S Chung, S C H Yu. Trilateral filtering for biomedical images. In Proc. IEEE Int. Symp. Biomedical Imaging. 2004: 820-823.

E P Bennett, L McMillan. Video enhancement using per-pixel virtual exposures. ACM Trans. Graph. 2005; 24(3): 845-852.

R Fattal, M Agrawala, S Rusinkiewicz. Multiscale shape and detail enhancement from multi-light image collections. ACM Trans. Graph., 2007; 26(3).

M Elad. Retinex by two bilateral filters. Scale-Space, Lecture Notes in Comput. Sci. 2005: 7-10.

S Paris, F Durand. A fast approximation of the bilateral filter using a signal processing approach. In Proc. Eur. Conf. Computer Vision. 2006: 568-580.

S Acton. Multigrid anisotropic diffusion. IEEE Trans. Image Process. 1998; 7(3): 280-291.

Color Test Images [Online]. Available: http://decsai.ugr.es/~javier/de-noise April 2008.




DOI: https://doi.org/10.11591/APTIKOM.J.CSIT.80

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