Combination of Wave Atoms Shrinkage with Bilateral Filtering for Oscillatory Textural Image Denoising

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Oscillatory textures are not only ubiquitous in natural images, but also in some inverse imaging problems involving oscillatory date, such as seismic databases, fingerprint images, et al. In this paper, we propose a three-step denoising scheme for for oscillatory textural images by combining bilateral filtering in spacial domain with wave atoms shrinkage method in transformed domain. That is, we first pre-process noisy image with bilateral filtering, then to process image with wave atoms shrinkage, finally post-process image with bilateral filtering again. Above all, we offer a reasonable interpretation of proposed method from the point of nonlinear diffusion filtering. Numerical experiments illustrate the good performance in comparison to the wave atoms shrinkage method and the bilateral filtering method by using two measures: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

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Advanced Materials Research (Volumes 291-294)

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2119-2124

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July 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] D.L. Donoho: IEEE Transactions on Information Theory Vol. 41(1995), p.613

Google Scholar

[2] J. Portilla, V. Strela, M.J. Wainwright, et. al: IEEE Trans Image Process Vol. 12(2003), p.1338

Google Scholar

[3] D.L. Donoho: SIAM Journal on Mathematical Analysis Vol. 31(2000), p.143

Google Scholar

[4] E.J. Candes, D.L. Donoho: Comm Pure Appl Math Vol. 57(2004), p.219

Google Scholar

[5] M. Aharon, M. Elad, and A.M. Bruckstein: IEEE Trans Image Process Vol. 54(2006), p.4311

Google Scholar

[6] C. Tomasi, et. al, in: IEEE InternationalConference on Computer Vision, Bombay, India, 1998.

Google Scholar

[7] D. Barash: IEEE Trans Pattern Anal Mach Intell, Vol. 24(2002), p.844

Google Scholar

[8] A. Buades, B. Coll, and J.M. Morel: Multiscale Modeling and Simulation Vol. 4(2005), p.490

Google Scholar

[9] K. Dabov, A. Foi, V. Katkovnik, et. al: IEEE Trans Image Process, Vol. 16(2007), p. (2080)

Google Scholar

[10] L. Demanet, L.X. Ying: Applied and Computational Harmonic Analysis, Vol.23(2007), p.368

Google Scholar

[11] S.M. Smith, ,J.M. Brady: International Journal of Computer Vision, Vol. 23(1997), p.45

Google Scholar

[12] M. Elad: IEEE Trans Image Process, Vol. 11(2002), p.1141

Google Scholar

[13] S. Paris, F. Durand: International Journal of Computer Vision, Vol. 81(2009), p.24

Google Scholar

[14] G. Vijaya, V. Vasudevan: European Journal of Scientific Research, Vol. 46(2010), p.331

Google Scholar

[15] Ming Zhang and B. K. Gunturk: IEEE Trans Image Process, Vol. 17(2008), p.2324

Google Scholar

[16] A. Wong: Signal Processing, Vol. 88(2008), p.1615

Google Scholar

[17] I.W. Selesnick, R.G. Baraniuk, et. al: IEEE Signal Process Mag, Vol. 22(2005), p.123

Google Scholar

[18] G. Hellwig: Partial Differential Equations(Teubner, Stuttgart, 1977).

Google Scholar

[19] F. Cattes, P. Lions, J. Morel, T. Coll: SIAM J Numer Anal, Vol. 29(1992), p.182

Google Scholar

[20] Jianwei Ma: Applied Physics Letters, Vol. 90(2007), p.264101

Google Scholar

[21] Z. Wang, A.C. Bovik, H.R. Sheikh, et.al: IEEE Trans Image Process, Vol. 13(2004), p.600

Google Scholar