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access icon openaccess Image denoising via an improved non-local total variation model

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References

    1. 1)
      • 1. Rafsanjani, H.K., Sedaaghi, M.H., Saryazdi, S.: ‘An adaptive diffusion coefficient selection for image denoising’, Digit. Signal Process., 2017, 64, pp. 7182.
    2. 2)
      • 2. Surya Prasath, V.B., Vorotnikov, D.: ‘Weighted and well-balanced anisotropic diffusion scheme for image denoising and restoration’, Nonlinear Anal. Real World Appl., 2014, 17, pp. 3346.
    3. 3)
      • 3. Yuan, J.: ‘Improved anisotropic diffusion equation based on new non-local information scheme for image denoising’, IET Comput. Vis., 2015, 9, (6), pp. 864870.
    4. 4)
      • 4. Rudin, L.I., Osher, S., Fatemi, E.: ‘Nonlinear total variation based noise removal algorithms’, Physica D, 1992, 60, (1–4), pp. 259268.
    5. 5)
      • 5. Zhang, W., Cao, Y., Zhang, R., et al: ‘Image denoising using total variation model guided by steerable filter’, Math. Problems Eng., 2014, 2014, pp. 111.
    6. 6)
      • 6. Yuan, Q., Zhang, L., Shen, H.: ‘Regional spatially adaptive total variation super-resolution with spatial information filtering and clustering’, IEEE Trans. Image Process., 2013, 22, (6), pp. 23272342.
    7. 7)
      • 7. Liu, C., Zou, H., Li, C., et al: ‘An adaptive texture-preserved image denoising model’, J. Ambient Intell. Human. Comput., 2015, 6, (5), pp. 689697.
    8. 8)
      • 8. Li, S., Wang, G., Zhao, X.: ‘Multiplicative noise removal via adaptive learned dictionaries and TV regularization’, Digit. Signal Process., 2016, 50, pp. 218228.
    9. 9)
      • 9. Buades, A., Coll, B., Morel, J.M.: ‘A review of image denoising algorithms, with a new one’, Multiscale Model. Simul., 2005, 4, (2), pp. 490530.
    10. 10)
      • 10. Li, H., Suen, C.Y.: ‘A novel non-local means image denoising method based on grey theory’, Pattern Recognit.., 2016, 49, pp. 237248.
    11. 11)
      • 11. Chan, S.H., Zickler, T., Lu, Y.M.: ‘Monte Carlo non-local means: random sampling for large-scale image filtering’, IEEE Trans. Image Process., 2014, 23, (8), pp. 37113725.
    12. 12)
      • 12. Liu, B., Sang, X., Xing, S., et al: ‘Noise suppression in brain magnetic resonance imaging based on non-local means filter and fuzzy cluster’, Optik, 2015, 126, (21), pp. 29552959.
    13. 13)
      • 13. He, N., Wang, J.B., Zhang, L.L., et al: ‘Non-local sparse regularization model with application to image denoising’, Multimed. Tools Appl., 2016, 75, (5), pp. 25792594.
    14. 14)
      • 14. Yang, S., Zhao, L., Wang, M., et al: ‘Dictionary learning and similarity regularization based image noise reduction’, J. Vis. Commun. Image R, 2013, 24, (2), pp. 181186.
    15. 15)
      • 15. Dabov, K., Foi, A., Katkovnik, V., et al: ‘Image denoising by sparse 3-D transform-domain collaborative filtering’, IEEE Trans. Image Process., 2007, 16, (8), pp. 20802095.
    16. 16)
      • 16. Yang, M., Liang, J., Zhang, J., et al: ‘Non-local means theory based Perona-Malik model for image denosing’, Neurocomputing, 2013, 120, pp. 262267.
    17. 17)
      • 17. Zhang, W., Li, J., Yang, Y.: ‘A class of nonlocal tensor telegraph-diffusion equations applied to coherence enhancement’, Comput. Math. Appl., 2014, 67, (8), pp. 14611473.
    18. 18)
      • 18. Gilboa, G., Osher, S.: ‘Nonlocal operators with applications to image processing’, Multiscale Model. Simul., 2008, 7, (3), pp. 10051028.
    19. 19)
      • 19. Lou, Y., Zhang, X., Osher, S., et al: ‘Image recovery via nonlocal operators’, J. Sci. Comput., 2010, 42, (2), pp. 185197.
    20. 20)
      • 20. Zhang, X., Burger, M., Bresson, X., et al: ‘Bregmanized nonlocal regularization for deconvolution and sparse reconstruction’, SIAM J. Imaging Sci., 2010, 3, (3), pp. 253276.
    21. 21)
      • 21. Liu, X., Huang, L.: ‘A new nonlocal total variation regularization algorithm for image denoising’, Math. Comput. Simul., 2014, 97, pp. 224233.
    22. 22)
      • 22. Elad, M., Aharon, M.: ‘Image denoising via sparse and redundant representations over learned dictionaries’, IEEE Trans. Image Process., 2006, 15, (12), pp. 37363745.
    23. 23)
      • 23. Gu, S., Xie, Q., Meng, D., et al: ‘Weighted nuclear norm minimization and its applications to low level vision’, Int. J. Comput. Vis., 2017, 121, (2), pp. 183208.
    24. 24)
      • 24. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600612.
    25. 25)
      • 25. Chang, L.C., El-Araby, E., Dang, V.Q., et al: ‘GPU acceleration of nonlinear diffusion tensor estimation using CUDA and MPI’, Neurocomputing, 2014, 135, pp. 328338.
    26. 26)
      • 26. Howison, M., Bethel, E.W.: ‘GPU-accelerated denoising of 3D magnetic resonance images’, J. Real-Time Image Process., 2017, 13, (4), pp. 713724.
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