Open Access
Issue
ITM Web Conf.
Volume 29, 2019
1st International Conference on Computational Methods and Applications in Engineering (ICCMAE 2018)
Article Number 01009
Number of page(s) 7
Section Applied/Computational Mathematics
DOI https://doi.org/10.1051/itmconf/20192901009
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. T.F. Chan, G. H. Golub, and P. Mulet, “ A nonlinear primal-dual method for total variation-based image restoration,” SIAM J. Sci. Comput., vol. 20, pp. 1964–1977, (1999) [CrossRef] [MathSciNet] [Google Scholar]
  8. A. Chambolle, “ An algorithm for total variation minimization and applications,” J. Math. Imaging Vision, pp. 89–97, (2004) [Google Scholar]
  9. S. Kim and H. Lim, “ A non-convex diffusion model for simultaneous image denoising and edge enhancement,” Electro. J. ofDiff. Eq., vol. 15, pp. 175–192, (2007) [Google Scholar]
  10. K. Krissian, R. Kikinis, C.-F. Westin, and K. Vosburgh, “ Speckle-constrained filtering of ultrasound images,” in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, Sand Diego, CA, Jun. 2005, vol. 2, pp. 547–552 [Google Scholar]
  11. K. Krissian, C.-F. Westin, R. Kikinis, and K. Vosburgh, “ Oriented Speckle Reducing Anisotropic Diffusion,” IEEE Trans. Image Process., vol. 16, no. 5, pp. 1412–1424, (2007) [CrossRef] [Google Scholar]
  12. H. Lim and T.N. Williams “ A non-standard anisotropic diffusion for speckle noise removal,” Journal of Systemics, Cybernetics and Informatics, vol. 5, no. 2, pp. 12–17, (2007) [Google Scholar]
  13. 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]
  14. A.B. Misra, E. Lockhart, and H. Lim, “ Total variation based denoising methods for speckle noise images,” Involve, a J. of Mathematics, vol. 10, no. 2, pp 327–344, (2016) [CrossRef] [Google Scholar]
  15. Y. Cha and S. Kim, “ Edge-forming methods for image zooming,” J. Math. Imaging Vision, vol. 25 pp. 353–364 (2006) [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.