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Hybrid Variational Model for Texture Image Restoration

Published online by Cambridge University Press:  07 September 2017

Liyan Ma*
Affiliation:
Institute of Microelectronics of Chinese Academy of Sciences, Beijing, China
Tieyong Zeng*
Affiliation:
Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China HKBU Institute of Research and Continuing Education, Shenzhen Virtual University Park, Shenzhen 518057, China
Gongyan Li*
Affiliation:
Institute of Microelectronics of Chinese Academy of Sciences, Beijing, China
*
*Corresponding author. Email addresses:maliyan@ime.ac.cn (L. Ma), zeng@hkbu.edu.hk (T. Zeng), ligongyan@ime.ac.cn (G. Li)
*Corresponding author. Email addresses:maliyan@ime.ac.cn (L. Ma), zeng@hkbu.edu.hk (T. Zeng), ligongyan@ime.ac.cn (G. Li)
*Corresponding author. Email addresses:maliyan@ime.ac.cn (L. Ma), zeng@hkbu.edu.hk (T. Zeng), ligongyan@ime.ac.cn (G. Li)
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Abstract

The hybrid variational model for restoration of texture images corrupted by blur and Gaussian noise we consider combines total variation regularisation and a fractional-order regularisation, and is solved by an alternating minimisation direction algorithm. Numerical experiments demonstrate the advantage of this model over the adaptive fractional-order variational model in image quality and computational time.

Type
Research Article
Copyright
Copyright © Global-Science Press 2017 

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References

[1] Afonso, M.V., Bioucas-Dias, J.M. and Figueiredo, M.A.T., Fast image recovery using variable splitting and constrained optimization, IEEE Trans. Image Process. 19, 10577149 (2010).Google Scholar
[2] Bai, J. and Feng, X.C., Fractional-order anisotropic diffusion for image denoising, IEEE Trans. Image Process. 16, 24922502 (2007).CrossRefGoogle ScholarPubMed
[3] Beck, A. and Teboulle, M., A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM J. Imaging Sci. 2, 183202 (2009).CrossRefGoogle Scholar
[4] Berkels, B., Burger, M., Droske, M., Nemitz, O. and Rumpf, M., Cartoon extraction based on anisotropic image classification vision, modeling, and visualization, Vision, Modeling, and Visualization, pp. 293300, Akademische Verlagsgesellschaft (2006).Google Scholar
[5] Boyd, S. and Vandenberghe, L., Convex Optimization, Cambridge University Press (2004).Google Scholar
[6] Bredies, K., Kunisch, K. and Pock, T., Total generalized variation, SIAM J. Imaging Sci. 3, 492526 (2010).Google Scholar
[7] Buades, A., Coll, B. and Morel, J.M., A review of image denoising algorithms, with a new one, Multiscale Model. Simul. 4, 490530 (2005).Google Scholar
[8] Buades, A., Le, T.M., More, J.M. and Vese, L., Fast cartoon+texture image filters, IEEE Trans. Image Process. 19, 19781986 (2010).Google Scholar
[9] Chambolle, A., An algorithm for total variation minimization and applications, J. Math. Imag. Vis. 20, 8997 (2004).Google Scholar
[10] Chambolle, A. and Lions, P.L., Image recovery via total variation minimization and related problems, Numer. Math. 76, 167188 (1997).CrossRefGoogle Scholar
[11] Chambolle, A. and Pock, T., A first-order primal-dual algorithm for convex problems with applications to imaging, J. Math. Imaging Vis. 40, 120145 (2011).Google Scholar
[12] Chan, R.H., Lanza, A., Morigi, S. and Sgallari, F., An adaptive strategy for the restoration of textured images using fractional order regularization, Numer. Math. Theor. Meth. Appl. 6, 276296 (2013).CrossRefGoogle Scholar
[13] Chan, T. and Zhou, H.M., Total variation wavelet thresholding, J. Sci. Comput. 32, 315341 (2007).CrossRefGoogle Scholar
[14] Chen, D., Chen, Y. and Xue, D., Three fractional-order TV-l2 models for image denoising, J. Comput. Inform. Syst. 9, 47734780 (2013).Google Scholar
[15] Chen, D., Chen, Y. and Xue, D., Fractional-order total variation image denoising based on proximity algorithm, Appl. Math. Comput. 257, 537545 (2015).Google Scholar
[16] Daubechies, I. and Teschke, G., Variational image restoration bymeans of wavelets: Simultaneous decomposition, deblurring, and denoising, Appl. Comput. Harmon. A. 19, 116 (2005).CrossRefGoogle Scholar
[17] Dong, B., Ji, H., Li, J, Shen, Z. and Xu, Y., Wavelet frame based blind image inpainting, Appl. Comput. Harmon. A. 32, 268279 (2012).Google Scholar
[18] Dong, Y., Hintermüler, M. and Neri, M., An efficient primal-dual method for L1TV image restoration, SIAM J. Imag. Sci. 2, 11681189 (2009).CrossRefGoogle Scholar
[19] Gilboa, G., Sochen, N. and Zeevi, Y.Y., Variational denoising of partly textured images by spatially varying constraints, IEEE Trans. Image Process. 15, 22812289 (2006).Google Scholar
[20] Goldstein, T. and Osher, S., The split Bregman algorithm for L1 regularized problems, SIAM J. Imag. Sci. 2, 323343 (2009).Google Scholar
[21] Grasmair, M., Locally adaptive total variation regularisation, in Scale Space and Variational Methods in Computer Vision, Tai, X., Morken, K., Lysaker, O.M. and Lie, K. (Eds.), pp. 331342, Springer (2009).Google Scholar
[22] Guo, X., Li, F. and Ng, M.K., A fast l1-TV algorithm for image restoration, SIAM J. Sci. Comput. 31, 23222341 (2009).CrossRefGoogle Scholar
[23] Lou, Y., Zeng, T., Osher, S. and Xin, J., A weighted difference of anisotropic and isotropic total variation model for image processing, SIAM J. Imag. Sci. 8, 17981823 (2015).Google Scholar
[24] Lou, Y., Zhang, X., Osher, S. and Bertozzi, A., Image recovery via nonlocal operators, J. Sci. Comput. 42, 185197 (2010).Google Scholar
[25] Ma, L., Ng, M., Yu, J. and Zeng, T., Efficient box-constrained TV-type-l1 algorithms for restoring images with impulse noise, J, Comput. Math. 31, 249270 (2013).Google Scholar
[26] Meyer, Y., Oscillating Patterns in Image Processing and in Some Nonlinear Evolution Equations. The Fifteenth Dean Jacquelines B. Lewis Memorial Lectures, American Mathematical Society (2001).Google Scholar
[27] Miller, K.S. and Ross, B., An Introduction to the Fractional Calculus and Fractional Differential Equations, Wiley Interscience (1993).Google Scholar
[28] Ng, M., Ngan, H., Yuan, X. and Zhang, W., Patterned fabric inspection and visualization by the method of image decomposition, IEEE Trans. Aotum. Sci. Eng. 11, 943947 (2013).Google Scholar
[29] Ng, M., Yuan, X. and Zhang, W., A coupled variational image decomposition and restoration model for blurred cartoon-plus-texture images with missing pixels, IEEE Trans. Image Process. 22, 22332246 (2013).Google Scholar
[30] Oldham, K.B.A. and Spanier, J.A., The Fractional Calculus: Theory and Applications of Differentiation and Integration to Arbitrary Order, Dover (2006).Google Scholar
[31] Osher, S., Sole, A. and Vese, L., Image decomposition and restoration using total variation minimization and the H1, Multiscale Modeling & Simulation 1, 349370 (2003).Google Scholar
[32] Rudin, L., Osher, S. and Fatemi, E., Nonlinear total variation based noise removal algorithms, Physica D 60, 259268 (1992).CrossRefGoogle Scholar
[33] Setzer, S., Steidl, G. and Teuber, T., nfimal convolution regularizations with discrete L1-type functionals, Comm. Math. Sci. 9, 797872 (2011).Google Scholar
[34] Tai, X.-C. and Wu, C., Augmented Lagrangian method, dual methods and split Bregman iteration for ROF model, SSVM 2009, LNCS 5567, 42, pp. 502513, Springer (2009) .Google Scholar
[35] Tikhonov, A. and Arsenin, V., Solution of Ill-Posed Problems, Winston and Sons (1977).Google Scholar
[36] Wang, W. and Ng, M.K., On algorithms for automatic deblurring from a single image, J. Comput. Math. 30, 80100 (2012).Google Scholar
[37] Wen, Y-W., Ng, M.K. and Huang, Y., Efficient total variation minimization methods for color image restoration, IEEE Trans. Image Process. 17, 20812088 (2008).Google Scholar
[38] Vese, L. and Osher, S., Modeling textures with total variation minimization and oscillating patterns in image processing, J. Sci. Computing 19, 553572 (2003).Google Scholar
[39] Wu, C., Zhang, J. and Tai, X.-C., Augmented lagrangian method for total variation restoration with non-quadratic fidelity, Inverse Problems Imag. 5, 237261 (2011).Google Scholar
[40] Yang, F., Chen, K. and Yu, B., Homotopy curve tracking for total variation image restoration, J. Comput. Math. 30, 177196 (2012).CrossRefGoogle Scholar
[41] Yang, J., Zhang, Y. and Yin, W., An efficient TVL1 algorithm for deblurring multichannel images corrupted by impulsive noise, SIAM J. Sci. Comput. 31, 28422865 (2009).Google Scholar
[42] You, Y.L. and Kaveh, M., Fourth-order partial differential equations for noise removal, IEEE Trans. Image Process. 9, 17231730 (2000).Google Scholar
[43] Zeng, T., Li, X. and Ng, M., Alternating minimization method for total variation based wavelet shrinkage model, Commun. Comput. Phys. 8, 976994 (2010).Google Scholar
[44] Zhang, X., Burger, M., Bresson, X. and Osher, S., Bregmanized nonlocal regularization for deconvolution and sparse reconstruction, SIAM J. Imag. Sci. 3, 253276 (2010).Google Scholar
[45] Zhang, J. and Chen, K., A total fractional-order variation model for image restoration with non-homogeneous boundary conditions and its numerical solution, SIAM J. Imag. Sci. 8, 24872518 (2015).Google Scholar
[46] Zhang, J., Wei, Z. and Xiao, L., Adaptive fractional-order multi-scale method for image denoising, SIAM J. Imag. Sci. 43, 3949 (2012).Google Scholar