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A Variational Model for Removing Multiple Multiplicative Noises

DOI: 10.4236/ojapps.2015.512075, PP. 783-796

Keywords: Noise Removal, Staircase Effect, Rayleigh Noise, Gamma Noise

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Abstract:

The problem of multiplicative noise removal has been widely studied in recent years. Many methods have been used to remove it, but the final results are not very excellent. The total variation regularization method to solve the problem of the noise removal can preserve edge well, but sometimes produces undesirable staircasing effect. In this paper, we propose a variational model to remove multiplicative noise. An alternative algorithm is employed to solve variational model minimization problem. Experimental results show that the proposed model can not only effectively remove Gamma noise, but also Rayleigh noise, as well as the staircasing effect is significantly reduced.

 References

[1]  Hou, Y.Q., Zhang, H., Zhang, H. and Zhang, L.Y. (2008) An Improved Adaptive Image Denoising Model Based on Total Variation. Journal of Northwest University (Natural Science Edition), 38, 371-373.
[2]  Guo, X.L., Wu, C.S., et al. (2011) Improved Adaptive Image Denoising Total Variation Regularization Model. Journal of WUT (Information &Management Engineering), 33, 200-202.
[3]  Peng, L., Fang, H., Li, G.Q. and Liu, Z.W. (2012) Remote-Sensing Image Denoising Using Partial Differential Equations and Auxiliary Images as Priors. IEEE Geoscience and Remote Sensing Letters, 9, 358-362.
http://dx.doi.org/10.1109/LGRS.2011.2168598
[4]  Zheng, M.J. and Xiao, P.Y. (2010) Analysis of a New Variational Model for Multiplicative Noise Removal. Journal of Mathematical Analysis and Applications, 362, 415-426.
http://dx.doi.org/10.1016/j.jmaa.2009.08.036
[5]  Shih, Y., Rei, C. and Wang, H. (2009) A Novel PDE Based Image Restoration: Convection-Diffusion Equation for Image Denoising. Journal of Computational and Applied Mathematics, 231, 771-779.
http://dx.doi.org/10.1016/j.cam.2009.05.001
[6]  Rudin, L., Lions, P.L. and Osher S. (2003) Multiplicative Denoising and Deblurring: Theory and Algorithms. Geometric Level Set Methods in Imaging, Vision, and Graphics. Springer, New York, 103-119.
http://dx.doi.org/10.1007/0-387-21810-6_6
[7]  Aubert, G. and Aujol, F. (2008) A Variational Approach to Removing Multiplicative Noise. SIAM Journal on Applied Mathematics, 68, 925-946.
http://dx.doi.org/10.1137/060671814
[8]  Huang, Y.-M., Ng, M.K. and Wen, Y.-W. (2009) A New Total Variation Method for Multiplicative Noise Removal. SIAM Journal on Imaging Sciences, 2, 20-40.
http://dx.doi.org/10.1137/080712593
[9]  Hu, X.G. and Lou, Y.F. (2014) A Novel Total Variational Model for Gamma Multiplicative Noise Removal. Journal of Sichuan University (Engineering Science Edition), 46, 59-65.
[10]  Shi, J. and Osher, S. (2008) A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model. SIAM Journal on Imaging Sciences, 1, 294-321.
http://dx.doi.org/10.1137/070689954
[11]  Rodrigues, I.C. and Sanches, J.M.R. (2011) Convex Total Variation Denoising of Poisson Fluorescence Confocal Images Anisotropic Filtering. IEEE Transactions on Image Processing, 20, 146-160.
http://dx.doi.org/10.1109/TIP.2010.2055879
[12]  Bioucas-Dias, J.M. and Figueiredo, M.A.T. (2010) Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization. IEEE Transactions on Image Processing, 19, 1720-1730.
http://dx.doi.org/10.1109/TIP.2010.2045029
[13]  Afonso, M. and Sanches, J.M. (2015) Image Reconstruction under Multiplicative Speckle Noise Using Total Variation. Contents List at Science Direct, 15, 200-213.
[14]  Eckstein, J. and Bertsekas, D. (1992) On the Douglas-Rachford Splitting Method and the Proximal Point Algorithm for Maximal Monotone Operators. Mathematical Programming, 55, 293-318.
http://dx.doi.org/10.1007/BF01581204
[15]  Bioucas, J. and Figueiredo, M. (2010) Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization. IEEE Transactions on Image Processing, 19, 1720-1730.
http://dx.doi.org/10.1109/TIP.2010.2045029
[16]  Yin, W., Osher, S., Goldfarb, D. and Drabon, J. (2008) Bregman Iterative Algorithms for -Minimization with Applications to Compressed Sensing. SIAM Journal on Imaging Sciences, 1, 143-168.
http://dx.doi.org/10.1137/070703983
[17]  Bonnans, J. and Jean, C.G. (2006) Numerical Optimization: Theoretical and Practical Aspects. Springer Press.
[18]  Seabra, J. and Sanches, J. (2008) Modeling Log-Compressed Ultrasound Images for Radio Frequency Signal Recovery. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, 20-25 August 2008, 426-429.
http://dx.doi.org/10.1109/iembs.2008.4649181
[19]  Cheng, W.B. and Cheng, J. (2011) Image Processing and Analysis. Science Press, Beijing.
[20]  Hao, Y. and Xu, J.L. (2014) An Effective Dual Method for Multiplicative Noise Removal. Journal of Visual Communication and Image Representation, 25, 306-312.
http://dx.doi.org/10.1016/j.jvcir.2013.11.004
[21]  Chen, H.Z., Song, J.P. and Tai, X.C. (2009) A Dual Algorithm for Minimization of the LLT Model. Advances in Computational Mathematics, 31, 115-130.
http://dx.doi.org/10.1007/s10444-008-9097-0
[22]  Chambole, A. (2004) An Algorithm for Total Variation Minimization and Applications. Journal of Mathematical Imaging and Vision, 20, 133-146.
http://dx.doi.org/10.1023/B:JMIV.0000011321.19549.88
[23]  Zheng, M.J. and Xiao, P.Y. (2011) A Variational Model to Remove the Multiplicative Noise in Ultrasound Images. Journal of Mathematical Imaging and Vision, 39, 62-74.
http://dx.doi.org/10.1007/s10851-010-0225-3
[24]  Grimmett, G. (1986) Probability: An Introduction. Oxford University Press, Oxford.
[25]  Zheng, J.M. and Zhu, W. (2000) Numerical Calculation Method. Chongqing University Press, Chongqing.
[26]  Byne, C.L. (2012) Alternating Minimization as Sequential Unconstrained Minimizations: A Survey. Journal of Optimization Theory and Applications, 156, 554-566.
[27]  Lv, X.G., Le, J., Huang, J. and Jun, L. (2013) A Fast High-Order Total Variation Minimization Method for Multiplicative Noise Removal. Mathematical Problems in Engineering, 2013, Article ID: 834035.
http://dx.doi.org/10.1155/2013/834035

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