Open Access
ITM Web Conf.
Volume 29, 2019
1st International Conference on Computational Methods and Applications in Engineering (ICCMAE 2018)
Article Number 01003
Number of page(s) 7
Section Applied/Computational Mathematics
Published online 15 October 2019
  1. D. Mumford, and J. Shah, “ Optimal approximations by piecewise smooth functions and variational problems,” Commun. Pure Appl. Math., vol. 42, pp. 577–685, (1989) [CrossRef] [MathSciNet] [Google Scholar]
  2. P. Perona, and J. Malik, “ Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 7, pp. 629–639, (1990) [CrossRef] [Google Scholar]
  3. L.I. Rudin, S. Osher, and E. Fatemi, “ Nonlinear total variation based noise removal algorithms,” Physica D, vol. 60, pp. 259–268, (1992) [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  4. F. Catté, P.-L. Lions, J.-M. Morel, and T. Coll, “ Image selective smoothing and edge detection by nonlinear diffusion,” SIAM J. Numer. Anal., vol. 29, no. 1, pp. 182–193, (1992) [CrossRef] [MathSciNet] [Google Scholar]
  5. L. Alvarez, P.-L. Lions, and J.-M. Morel, “ Image selective smoothing and edge detection by nonlinear diffusion. ii,” SIAM J. Numer. Anal., vol. 29, no. 3, pp. 845–866, (1992) [CrossRef] [Google Scholar]
  6. A. Marquina, and S. Osher, “ Explicit algorithms for a new time dependent model based on level set motion for nonlinear deblurring and noise removal,” SIAM J. Sci. Comput, vol. 22, pp. 387–405, (1999) [CrossRef] [Google Scholar]
  7. L. Yaroslavsky, Digital Picture Processing - An Introduction (Springer Verlag, 1985) [CrossRef] [Google Scholar]
  8. S.M. Smith, and J.M. Brady, “ SUSAN - A new approach to low level image processing,” Int. J. Comput. Vis., vol. 23, no. 1, pp. 45–78, (1997) [CrossRef] [Google Scholar]
  9. J. van de Weijer and R. van den Boomgaard, “ Local mode filtering,” in IEEE Computer Soc. Conf. Computer Vis. Pattern Recognit., Kauai, HI, Dec. 2001, pp. 428–433. [Google Scholar]
  10. M. Elad, “ On the origin of the bilateral filter and ways to improve it,” IEEE Trans. Image Process., vol. 11, no. 10, pp. 1141–1151, (2002) [CrossRef] [Google Scholar]
  11. A. Buades, B. Coll, and J. M. Morel, “ A review of image denoising algorithms, with a new one,” Multiscale Model. Simul., vol. 4, no. 2, pp. 490–530 (2005) [CrossRef] [Google Scholar]
  12. S. Kindermann, S. Osher, and P.W. Jones, “ Deblurring and denoising of images by nonlocal functionals,” Multiscale Model. Simul., vol. 4, no. 4, pp. 1091–1115, (2005) [CrossRef] [Google Scholar]
  13. G. Gilboa, and S. Osher, “ Nonlocal operators with applications to image processing.” Multiscale Model. Simul., vol. 7, no. 3, pp. 1005–1028, (2008) [CrossRef] [Google Scholar]
  14. A.B. Misra, and H. Lim, “ Nonlocal speckle denoising model based on non-linear partial differential equations,” Information Systems Design and Intelligent Applications, pp 165176, (2015) [Google Scholar]
  15. P. Coupé, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot, “ An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images,” IEEE Trans. Med Imaging, vol. 27, no. 4, pp. 425–441, (2008) [CrossRef] [Google Scholar]
  16. M. Mahmoudi, and G. Sapiro, “ Fast image and video denoising via nonlocal means of similar neighborhoods,” IEEE Signal Process. Lett., vol. 12, no. 12, pp. 839–842, (2005) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.