[1] L. Rudin, S. Osher, E. Fatemi. Nonlinear total variation based noise removal algorithms [J]. Physica D, 1992, 60(1-4): 259~268
[2] Y. M. Huang, K. N. G. Micheal, Y. W. Wen. A fast total variation minimization method for image restoration [J]. Mutiscale Model. Simul., 2008, 7(2): 774~795
[3] Wenze Shao, Zhihui Wei. Edge-and-corner preserving regularization for image interpolation and reconstruction [J]. Image and Vision Computing, 2008, 26(12): 1591~1606
[4] A. Marquina, S. J. Osher. Image super-resolution by TV-regularization and Bregman iteration [J]. J. Science Computing, 2008, 37(3): 367~382
[5] M. Bertalm′Io, V. Caselles, B. Roug′e et al.. TV based image restoration with local constraints [J]. J. Scientific Computing, 2003, 19(1-3): 95~122
[6] K. Chen, X-C Tai. A nonlinear multigrid method for total variation minimization from image restoration [J]. J. Scientific Computing, 2007, 33(2): 115~138
[7] Zhou Mouyan. De-Convolution and Signal Restoration [M]. Beijing: National Denfence Industry Press, 2001. 3
[8] M. K. Ng, H. F. Shen, E. Y. Lam et al.. A total variation regularization based super-resolution reconstruction algorithm for digital video [J]. EURASIP Journal on Applied Signal Processing, 2007, Article ID 74585
[9] J. Carter. Dual Methods for Total Variation-Based Image Restoration. [D]. Los Angeles: University of California, 2002
[10] A. Chambolle. An algorithm for total variation minimization and application [J]. J. Mathematical Imaging Vision, 2004, 20(1): 89~97