|
[1] Hong, Q., Lai, M.J. and Wang, J., 2021. The convergence of a numerical method for total variation flow. Journal of Algorithms and Computational Technology, 15, p.17483026211011323. [2] Tai, X.C., Winther, R., Zhang, X. and Zheng, W., 2022. A uniform preconditioner for a Newton algorithm for total-variation minimization and minimumsurface problems. arXiv preprint arXiv:2208.01390. [3] Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4), pp.259-268. [4] Tong, Q., Liang, G. and Bi, J., 2022. Calibrating the adaptive learning rate to improve convergence of ADAM. Neurocomputing, 481, pp.333-356. [5] Bartels, S., Nochetto, R.H. and Salgado, A.J., 2014. Discrete total variation flows without regularization. SIAM Journal on Numerical Analysis, 52(1), pp.363-385. [6] Dobson, D.C. and Vogel, C.R., 1997. Convergence of an iterative method for total variation denoising. SIAM Journal on Numerical Analysis, 34(5), pp.1779- 1791. [7] HECHT, Frédéric. New development in FreeFem++. Journal of numerical mathematics, 2012, vol. 20, no 3-4, p. 251-266. [8] Steidl, G., Weickert, J., Brox, T., Mrázek, P. and Welk, M., 2004. On the equivalence of soft wavelet shrinkage, total variation diffusion, total variation regularization, and SIDEs. SIAM Journal on Numerical Analysis, 42(2), pp.686- 713.
|