References

• 1. 1)
     • 12. Ojo, O.A., Kwaaitaal-Spassova, T.G.: ‘An algorithm for integrated noise reduction and sharpness enhancement’, IEEE Trans. Consum. Electron., 2000, 46, (3), pp. 474–480.
  2. 2)
     • 34. Miller, J., Thomas, J.: ‘Detectors for discrete-time signals in non-Gaussian noise’, IEEE Trans. Inf. Theory, 1972, 18, (2), pp. 241–250.
  3. 3)
     • 37. Kennedy, J., Eberhart, R.: ‘Particle swarm optimization’. IEEE Int. Conf. Neural Networks, 1995, pp. 1942–1948.
  4. 4)
     • 33. Easley, G.R., Labate, D.: ‘Multiscale analysis for multivariate data’ (Birkhäuser Boston, 2012).
  5. 5)
     • 21. Anderson, D.N., Arnold, B.C.: ‘Linnik distributions and processes’, J. Appl. Probab., 1993, 30, (2), pp. 330–340.
  6. 6)
     • 35. Liu, X., Tanaka, M., Okutomi, M.: ‘Single-image noise level estimation for blind denoising’, IEEE Trans. Image Process., 2013, 22, (12), pp. 5226–5237.
  7. 7)
     • 42. Goodenough, D.G., Han, T.: ‘Reducing noise in hyperspectral data – a nonlinear data series analysis approach’. IEEE WHISPERS'09. First Workshop on Hyperspectral Image Signal Processing, Grenoble, France, 2009, pp. 1–4.
  8. 8)
     • 20. Linnik, Y.V.: ‘Linear forms and statistical criteria, i, ii’, Selected Trans. Math. Stat. Probab., 1953, 3, pp. 41–90.
  9. 9)
     • 7. Buades, A., Coll, B., Morel, J.-M.: ‘A review of image denoising algorithms, with a new one’, Multiscale Model. Simul., 2005, 4, (2), pp. 490–530.
  10. 10)
      • 13. Rieder, P., Scheffler, G.: ‘New concepts on denoising and sharpening of video signals’, IEEE Trans. Consum. Electron, 2001, 47, (3), pp. 666–671.
  11. 11)
      • 43. Makitalo, M., Foi, A.: ‘Noise parameter mismatch in variance stabilization, with an application to Poisson-Gaussian noise estimation’, IEEE Trans. Image Process., 2014, 12, (23), pp. 5348–5359.
  12. 12)
      • 5. Luisier, F., Blu, T., Unser, M., et al: ‘A new sure approach to image denoising: interscale orthonormal wavelet thresholding’, IEEE Trans. Image Process., 2007, 16, (3), pp. 593–606.
  13. 13)
      • 39. Zhan, Z.-H., Zhang, J., Li, Y., et al: ‘Adaptive particle swarm optimization’, IEEE Trans. Syst. Man Cybern. B (Cybern.), 2009, 39, (6), pp. 1362–1381.
  14. 14)
      • 25. Baringhaus, L., Grübel, R.: ‘On a class of characterization problems for random convex combinations’, Ann. Inst. Stat. Math., 1997, 49, (3), pp. 555–567.
  15. 15)
      • 16. Burian, A., Kuosmanen, P.: ‘Tuning the smoothness of the recursive median filter’, IEEE Trans. Signal Process., 2002, 50, (7), pp. 1631–1639.
  16. 16)
      • 36. Ou, C., Lin, W.: ‘Comparison between PSO and GA for parameters optimization of PID controller’. Int. Conf. Mechatronics and Automation, 2006.
  17. 17)
      • 15. Núñez, R.C., Gonzalez, J.G., Arce, G.R., et al: ‘Fast and accurate computation of the myriad filter via branch-and-bound search’, IEEE Trans. Signal Process., 2008, 56, (7), pp. 3340–3346.
  18. 18)
      • 27. Rossberg, H.J.: ‘Positive definite probability densities’, Theory Probab Appl., 1991, 35, (1), pp. 169–174.
  19. 19)
      • 28. Kalluri, S., Arce, G.R.: ‘Robust frequency-selective filtering using weighted myriad filters admitting real-valued weights’, IEEE Trans. Signal Process., 2001, 49, (11), pp. 2721–2733.
  20. 20)
      • 32. Milanfar, P.: ‘A tour of modern image filtering: new insights and methods, both practical and theoretical’, IEEE Signal Process. Mag., 2013, 30, (1), pp. 106–128.
  21. 21)
      • 40. Jansen, P.W., Perez, R.E.: ‘Constrained structural design optimization via a parallel augmented Lagrangian particle swarm optimization approach’, Comput. Struct, 2011, 89, (13), pp. 1352–1366.
  22. 22)
      • 14. Gonzalez, J.G., Arce, G.R.: ‘Optimality of the myriad filter in practical impulsive-noise environments’, IEEE Trans. Signal Process., 2001, 49, (2), pp. 438–441.
  23. 23)
      • 4. Sendur, L., Selesnick, I.W.: ‘Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency’, IEEE Trans. Signal Process., 2002, 50, (11), pp. 2744–2756.
  24. 24)
      • 1. Chatterjee, P., Milanfar, P.: ‘Patch-based near-optimal image denoising’, IEEE Trans. Image Process., 2012, 21, (4), pp. 1635–1649.
  25. 25)
      • 3. Pizurica, A., Philips, W.: ‘Estimating the probability of the presence of a signal of interest in multiresolution single-and multiband image denoising’, IEEE Trans. Image Process., 2006, 15, (3), pp. 654–665.
  26. 26)
      • 6. Blu, T., Luisier, F.: ‘The sure-let approach to image denoising’, IEEE Trans. Image Process., 2007, 16, (11), pp. 2778–2786.
  27. 27)
      • 41. Grahn, H.F., Geladi, P.: ‘Techniques and applications of hyperspectral image analysis’ (John Wiley and Sons Ltd, Chichester, 2007).
  28. 28)
      • 29. Forbes, C., Evans, M., Hastings, N., et al: ‘Statistical distributions’ (John Wiley & Sons, 2011).
  29. 29)
      • 22. Kozubowski, T.J.: ‘The inner characterization of geometric stable laws’, Stat. Decisions. Int. J. Stochastic Method Mod., 1994, 12, (3), pp. 307–322.
  30. 30)
      • 17. Rider, P.R.: ‘Generalized Cauchy distributions’, Ann. Ins. Stat. Math., 1957, 9, (1), pp. 215–223.
  31. 31)
      • 9. Dabov, A., Katkovnik, V., Egiazarian, K.: ‘Image denoising by sparse 3D transform-domain collaborative filtering’, IEEE Trans. Image Process., 2007, 16, (9), pp. 2080–2095.
  32. 32)
      • 10. Dugad, R., Ahuja, N.: ‘Video denoising by combining Kalman and wiener estimates in: image process’. ICIP 99, Proc. Int. Conf. IEEE, 1999, vol. 4, pp. 152–156.
  33. 33)
      • 2. Portilla, J., Strela, V., Wainwright, M.J., et al: ‘Image denoising using scale mixtures of Gaussians in the wavelet domain’, IEEE Trans. Image Process., 2003, 12, (11), pp. 1338–1351.
  34. 34)
      • 8. Xiong, B., Yin, Z.: ‘A universal denoising framework with a new impulse detector and nonlocal means’, IEEE Trans. Image Process., 2012, 21, (4), pp. 1663–1675.
  35. 35)
      • 44. Makitalo, M., Foi, A.: ‘Optimal inversion of the Anscombe transformation in low-count Poisson image denoising’, IEEE Trans. Image Process., 2011, 20, (1), pp. 99–109.
  36. 36)
      • 45. Makitalo, M., Foi, A., Katkovnik, V., et al: ‘Practical Poissonian-Gaussian noise modeling and fitting for single-image rawdata’, IEEE Trans. Image Process., 2008, 17, (10), pp. 1737–1754.
  37. 37)
      • 24. Mittnik, S., Rachev, S.T.: ‘Modeling asset returns with alternative stable distributions’, Econ. Rev., 1993, 12, (3), pp. 261–330.
  38. 38)
      • 31. Soltani, A., Tafakori, L.: ‘A class of continuous Kernels and Cauchy type heavy tail distributions’, Stat. Probab. Lett., 2013, 83, (4), pp. 1018–1027.
  39. 39)
      • 18. Arce, G.R.: ‘Nonlinear signal processing: a statistical approach’ (John Wiley & Sons, 2005).
  40. 40)
      • 11. Miao, Z., Jiang, X.: ‘Weighted iterative truncated mean filter’, IEEE Trans. Signal Process., 2013, 61, (16), pp. 4149–4160.
  41. 41)
      • 26. Rossberg, H.: ‘Positiv definite verteilungsdichten’ (Akademie-Verlag, Berlin, 1991).
  42. 42)
      • 19. Aysal, T.C., Barner, K.E.: ‘Meridian filtering for robust signal processing’, IEEE Trans. Signal Process., 2007, 55, (8), pp. 3949–3962.
  43. 43)
      • 23. Mittnik, S., Rachev, S.T.: ‘Alternative multivariate stable distributions and their applications to financial modeling in: stable processes and related topics’ (Springer, 1991), pp. 107–119.
  44. 44)
      • 30. Kozubowski, T.J.: ‘Exponential mixture representation of geometric stable distributions’, Ann. Inst. Stat. Math., 2000, 52, (2), pp. 231–238.
  45. 45)
      • 38. Clerc, M., Kennedy, J.: ‘The particle swarm-explosion, stability, and convergence in a multidimensional complex space’, IEEE Trans. Evol. Comput., 2002, 6, (1), pp. 58–73.