Все выпуски

Список литературы:

1. М. Абрамовиц, И. Стиган. Справочник по специальным функциям. — Москва: Изд. «Наука», 1979.
2. A. Abdi, et al. On the estimation of the K parameter for the Rice fading distribution // IEEE Commun. Lett. — 2001. — V. 5, no. 3. — P. 92–94. — DOI: 10.1109/4234.913150.
3. S. Aja-Fernandez, C. Alberola-Lopez, C.-F. Westin. Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach // IEEE Transactions on Image Processing. — 2008. — V. 17, no. 8. — P. 1383–1398. — DOI: 10.1109/TIP.2008.925382. — MathSciNet: MR2516906. — ads: 2008ITIP...17.1383A.
4. D. Barash. A fundamental relationship between bilateral filtering, adaptive smoothing and the nonlinear diffusion equation // IEEE Trans. PAMI. — 2002. — V. 24, no. 6. — P. 844–847. — DOI: 10.1109/TPAMI.2002.1008390.
5. S. Basu, et al. Rician noise removal in diffusion tensor MRI / Medical Image Computing and Computer-Assisted Intervention — MICCAI. — Berlin: Springer-Verlag, 2006. — P. 117–125.
6. T. R. Benedict, T. T. Soong. The joint estimation of signal and noise from the sum envelope // IEEE Trans. Inf. Theory. — 1967. — V. IT-13, no. 3. — P. 447–454. — DOI: 10.1109/TIT.1967.1054037.
7. A. Buades, Coll B., J. M. Morel. A review of image denoising algorithms, with a new one // Multiscale Model Simul. — 2005. — V. 4. — P. 490–530. — MathSciNet: MR2162865.
8. C. F.M. Carobbi, M. Cati. The absolute maximum of the likelihood function of the Rice distribution: existence and uniqueness // IEEE Trans. on Instrumentation and Measurement. — 2008. — V. 57, no. 4. — P. 682–689. — DOI: 10.1109/TIM.2007.913823.
9. D. Comaniciu, P. Meer. Mean shift: A robust approach toward feature space analysis // IEEE Trans. PAMI. — 2002. — V. 24, no. 5. — P. 603–619. — DOI: 10.1109/34.1000236.
10. I. Delakis, et al. Wavelet based denoising algorithm for images acquired with parallel magnetic resonance imaging (MRI) // Phys. Med. Biol. — 2007. — V. 52. — P. 3741–3751. — DOI: 10.1088/0031-9155/52/13/006.
11. L. Gang, X. Lei, C. Xuequan. Overview of the Applications of Curvelet Transform in Image Processing // Journal of Computer Reasearch and Development. — 2005. — P. 1331–1337.
12. S. J. Garnier, G. L. Bilbro. Magnetic resonance image restoration // J. Math. Imag., Vision. — 1995. — V. 5. — P. 7–19. — DOI: 10.1007/BF01250250.
13. G. Gerig, O. Kubler, R. Kikinis, F. A. Jolesz. Nonlinear anisotropic filtering of MRI data // IEEE Trans. Med. Imag. — 1992. — V. 11. — P. 221–232. — DOI: 10.1109/42.141646.
14. L. He, I. R. Greenshields. A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images // IEEE Trans Med Imaging. — 2009. — V. 28. — P. 165–172. — DOI: 10.1109/TMI.2008.927338.
15. Jianwei Ma, G. Plonka. The Curvelet Transform // Signal Processing Magazine, IEEE. — 2010. — V. 27, no. 2. — P. 118–133. — DOI: 10.1109/MSP.2009.935453. — ads: 2010ISPM...27..115J.
16. C. Koay, et al. A signal transformational framework for breaking the noise floor and its applications in MRI // Journal of Magnetic Resonance. — 2009. — V. 197. — P. 108–119. — DOI: 10.1016/j.jmr.2008.11.015. — ads: 2009JMagR.197..108K.
17. K. Krissian, S. Aja-Fernandez. Noise driven anisotropic diffusion filtering of MRI // IEEE Trans. Imag. Proc. — 2009. — V. 18. — P. 2265–2274. — DOI: 10.1109/TIP.2009.2025553. — MathSciNet: MR2789238. — ads: 2009ITIP...18.2265K.
18. M. Lysaker, et al. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time // IEEE Trans. Imag. Proc. — 2003. — V. 12, no. 12. — P. 1579–1590. — DOI: 10.1109/TIP.2003.819229. — ads: 2003ITIP...12.1579L.
19. J. V. Manjon, et al. Adaptive non local means denoising of MR images with spatially varying noise levels // J. Magn Reson Imaging. — 2010. — V. 31. — P. 192–203. — DOI: 10.1002/jmri.22003.
20. J. V. Manjon, et al. MRI denoising using non local means // Medical Image Analysis. — 2008. — V. 12. — P. 514–523. — DOI: 10.1016/j.media.2008.02.004.
21. G. McGibney, M. R. Smith. An unbiased signal-to-noise ratio measure for magnetic resonance images // Med. Phys. — 1993. — V. 20, no. 4. — P. 1077 — 1078. — DOI: 10.1118/1.597004.
22. R. D. Nowak. Wavelet based Rician noise removal for magnetic resonance images // IEEE Trans. Image Processing. — 1999. — V. 10, no. 8. — P. 1408–1419. — DOI: 10.1109/83.791966. — ads: 1999ITIP....8.1408N.
23. J.H. Jr. Park. Moments of generalized Rayleigh distribution // Q. Appl. Math. — 1961. — V. 19, no. 1. — P. 45–49. — DOI: 10.1090/qam/119222. — MathSciNet: MR0119222.
24. P. Perona, J. Malik. Scale-Space and Edge Detection Using Anisotropic Diffusion // IEEE Transactions on Pattern Analysis and Machine Intelligence. — 1990. — V. 12, no. 7. — P. 629–639. — DOI: 10.1109/34.56205.
25. A. Pizurica, et al. A versatile wavelet domain filtration technique for medical imaging // IEEE Trans. Med. Imaging. — 2003. — V. 22. — P. 323–331.
26. J. Rajan, B. Jeurissen, M. Verhoye, J. Van Audekerke, J. Sijbers. Maximum likelihood estimation based denoising of magnetic resonance images using restricted local neighborhoods // Physics in Medicine and Biology. — 2011. — V. 56, no. 16. — P. 5221–5234. — DOI: 10.1088/0031-9155/56/16/009. — ads: 2011PMB....56.5221R.
27. S. O. Rice. Mathematical Analysis of Random Noise // Bell System Technical Journal. — 1945. — V. 24. — P. 46–156. — DOI: 10.1002/j.1538-7305.1945.tb00453.x. — MathSciNet: MR0011918.
28. P. K. Saha, J. K. Udupa. Scale-based Diffusive Image Filtering Preserving Boundary Sharpness and Fine Structures // IEEE Trans. Med. Imaging. — 2001. — V. 20, no. 11. — P. 1140–1155. — DOI: 10.1109/42.963817.
29. J. Sijbers, A. J. den Dekker. Maximum Likelihood estimation of signal amplitude and noise variance from MR data // Magn. Reson. Med. — 2004. — V. 51, no. 3. — P. 586 — 594. — DOI: 10.1002/mrm.10728.
30. J. Sijbers, A. J. den Dekker, P. Scheunders, D. V. Dyck. Maximum-Likelihood Estimation of Rician Distribution Parameters // IEEE Transactions on Medical Imaging. — 1998. — V. 17, no. 3. — P. 357 — 361. — DOI: 10.1109/42.712125.
31. J.-L. Starck, E. J. Candès, D. L. Donoho. The curvelet transform for image denoising // IEEE Trans. Image Process. — 2002. — V. 11, no. 6. — P. 670–684. — DOI: 10.1109/TIP.2002.1014998. — MathSciNet: MR1929403. — ads: 2002ITIP...11..670S.
32. K.K. Talukdar, W.D. Lawing. Estimation of the parameters of Rice distribution // J. Acoust. Soc. Amer. — 1991. — V. 89, no. 3. — P. 1193–1197. — DOI: 10.1121/1.400532. — ads: 1991ASAJ...89.1193T.
33. N.A. Thacker, J.V. Manjon, P.A. Bromiley. A Statistical Interpretation of Non-Local Means // IET Computer Vision. — 2010. — V. 4, no. 3. — P. 162–172. — DOI: 10.1049/iet-cvi.2008.0076. — MathSciNet: MR2761197.
34. C. Tomasi, R. Manduchi. Bilateral filtering of gray and color images / Proceedings of the Sixth IEEE International Conference on Computer Vision. — 1998. — P. 839–846. — Bombay, India.
35. N. Wiest-Daessle, et al. Rician noise removal by Non-Local Means filtering for low Signal-to-Noise ration MRI: Applications to DT-MRI / Medical Image Computing and Computer-Assisted Intervention — MICCAI. — Berlin: Springer-Verlag, 2008. — P. 171–179.
36. J. C. Wood, K. M. Johnson. Wavelet Packet Denoising of Magnetic Resonance Images: Importance of Rician Noise at Low SNR // Magn Reson Med. — 1999. — V. 41, no. 3. — P. 631–635. — DOI: 10.1002/(SICI)1522-2594(199903)41:3<631::AID-MRM29>3.0.CO;2-Q.
37. T.V. Yakovleva, N. S. Kulberg. Noise and Signal Estimation in MRI: Two-Parametric Analysis of Rice-Distributed Data by Means of the Maximum Likelihood Approach // American Journal of Theoretical and Applied Statistics. — 2013. — V. 2, no. 3. — P. 67–79. — DOI: 10.11648/j.ajtas.20130203.15.
38. Y. You, M. Kaveh. Fourth order partial differential equations for noise removal // IEEE Trans. Imag. Proc. — 2000. — V. 9(10). — P. 1723–1730. — DOI: 10.1109/83.869184. — MathSciNet: MR1807566. — ads: 2000ITIP....9.1723Y.