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A block nonlocal TV method for image restoration. (English) Zbl 1437.94021

MSC:
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
68U10 Computing methodologies for image processing
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[1] P. Arias, G. Facciolo, V. Caselles, and G. Sapiro, A variational framework for exemplar-based image inpainting, Int. J. Comput. Vis., 93 (2011), pp. 319–347. · Zbl 1235.94015
[2] J. Biemond, A. M. Tekalp, and R. L. Lagendijk, Maximum likelihood image and blur identification: A unifying approach, Opt. Eng., 29 (1990), pp. 422–435.
[3] A. Buades, B. Coll, and J.-M. Morel, A non-local algorithm for image denoising, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, Vol. 2, IEEE, Computer Society, Los Alamitos, CA, 2005, pp. 60–65. · Zbl 1108.94004
[4] A. Buades, B. Coll, and J. M. Morel, A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 4 (2005), pp. 490–530. · Zbl 1108.94004
[5] P. Chatterjee and P. Milanfar, Clustering-based denoising with locally learned dictionaries, IEEE Trans. Image Process., 18 (2009), pp. 1438–1451. · Zbl 1371.94081
[6] P. Chatterjee and P. Milanfar, Patch-based near-optimal image denoising, IEEE Trans. Image Process., 21 (2012), pp. 1635–1649. · Zbl 1373.94069
[7] K. Dabov, A. Foi, and K. Egiazarian, Video denoising by sparse 3d transform-domain collaborative filtering, 2007 15th European in Signal Processing Conference, IEEE, Poznań, Poland, 2007, pp. 145–149.
[8] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising by sparse 3-d transform-domain collaborative filtering, IEEE Trans. Image Process., 16 (2007), pp. 2080–2095.
[9] W. Dong, X. Li, L. Zhang, and G. Shi, Sparsity-based image denoising via dictionary learning and structural clustering, in IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2011, IEEE, Piscataway, NJ, 2011, pp. 457–464.
[10] G. Gilboa and S. Osher, Nonlocal linear image regularization and supervised segmentation, Multiscale Model. Simul., 6 (2007), pp. 595–630. · Zbl 1140.68517
[11] G. Gilboa and S. Osher, Nonlocal operators with applications to image processing, Multiscale Model. Simul., 7 (2009), pp. 1005–1028. · Zbl 1181.35006
[12] T. Goldstein and S. Osher, The split Bregman method for L1-regularized problems, SIAM J. Imaging Sci., 2 (2009), pp. 323–343. · Zbl 1177.65088
[13] H. Ji, S. Huang, Z. Shen, and Y. Xu, Robust video restoration by joint sparse and low rank matrix approximation, SIAM J. Imaging Sci., 4 (2011), pp. 1122–1142. · Zbl 1234.68451
[14] V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, From local kernel to nonlocal multiple-model image denoising, Int. J. Comput. Vis., 86 (2010), pp. 1–32.
[15] S. Kindermann, S. Osher, and P. W. Jones, Deblurring and denoising of images by nonlocal functionals, Multiscale Modeling & Simulation, 4 (2005), pp. 1091–1115. · Zbl 1161.68827
[16] M. Lindenbaum, M. Fischer, and A. Bruckstein, On Gabor’s contribution to image enhancement, Pattern Recognit., 27 (1994), pp. 1–8.
[17] J. Liu, H. Huang, Z. Huan, and H. Zhang, Adaptive variational method for restoring color images with high density impulse noise, Int. J. Comput. Vis., 90 (2010), pp. 131–149.
[18] J. Liu, X.-C. Tai, H. Huang, and Z. Huan, A weighted dictionary learning model for denoising images corrupted by mixed noise, IEEE Trans. Image Process., 22 (2013), pp. 1108–1120. · Zbl 1373.94256
[19] J. Liu and H. Zhang, Image segmentation using a local GMM in a variational framework, J. Math. Imaging Vis., 46 (2013), pp. 161–176. · Zbl 1310.94018
[20] M. Nikolova, A variational approach to remove outliers and impulse noise, J. Math. Imaging Vision, 20 (2004), pp. 99–120. · Zbl 1366.94065
[21] P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Mach. Intell., 12 (1990), pp. 629–639.
[22] L. Rudin, S. Osher, et al., Total variation based image restoration with free local constraints, in IEEE International Conference Image Processing, 1994, Vol. 1, IEEE, Computer Society, Los Alamitos, CA, 1994, pp. 31–35.
[23] L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Phys. D. 60 (1992), pp. 259–268. · Zbl 0780.49028
[24] A. Singer, Y. Shkolnisky, and B. Nadler, Diffusion interpretation of nonlocal neighborhood filters for signal denoising, SIAM J. Imaging Sci., 2 (2009), pp. 118–139. · Zbl 1175.62102
[25] B. Wahlberg, S. Boyd, M. Annergren, and Y. Wang, An ADMM algorithm for a class of total variation regularized estimation problems, in Proceedings of the 16th IFAC Symposium on System Identification, Brussels, Belgium, 2012, pp. 83–88.
[26] C. Wu and X.-C. Tai, Augmented Lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models, SIAM J. Imaging Sci., 3 (2010), pp. 300–339. · Zbl 1206.90245
[27] L. P. Yaroslavsky and L. Yaroslavskij, Digital picture processing. An introduction, Springer Ser. Inform. Sci. 9, Springer, Berlin, 1985. · Zbl 0585.94001
[28] D. Zoran and Y. Weiss, From learning models of natural image patches to whole image restoration, in 2011 IEEE International Conference on Computer Vision, ICCV, IEEE, Piscataway, NJ, 2011, pp. 479–486.
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