ITM Web Conf.
Volume 29, 20191st International Conference on Computational Methods and Applications in Engineering (ICCMAE 2018)
|Number of page(s)||7|
|Published online||15 October 2019|
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- A. Chambolle, “ An algorithm for total variation minimization and applications,” J. Math. Imaging Vision, pp. 89–97, (2004) [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Y. Cha and S. Kim, “ Edge-forming methods for image zooming,” J. Math. Imaging Vision, vol. 25 pp. 353–364 (2006) [CrossRef] [Google Scholar]
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