[1] Candes E J, Donoho D L. Curvelets-A surprisingly effective non-adaptive representation for objects with edges[C]. Nashville, TN: Vanderbilt University Press, 2000: 105-120
[2] Candes E J, Donoho D L. New tight frames of curvelets and optimal representations of objects with C2 singularities[J]. Commun Pure Appl. Math, 2004, 57(2): 219-266
[3] Candes E J, Demanet L, Donoho D L. Fast discrete curvelet transforms[R]. Applied and Computational Mathematics. California Institute of Technology, 2005: 1-43
[4] Starck J L, Candes E J, Donoho D L. The curvelet transform for image denoising[J]. IEEE Trans. Image Processing, 2002, 11(6): 670-684
[5] Yang Jiahong, Xu Canhui, Wang Yaonan. Image de-noising algorithm based on fast curvelet transform[J]. Comput. Engin. Appl., 2007, 43(6): 31-33
[7] Xiao Xiaokui, Li Shaofa. Edge-preserving image denoising method using Curvelet transform[J]. J. China Inst. Commun. 2004, 25(2): 9-15
[8] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12(7): 629-639
[9] Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms [J]. Physica D, 1992, 60(1/4): 259-268
[10] Rudin L, Osher S. Total variation based image restoration with free local constraints[C]. Proceedings of the IEEE International Conference on Image Processing, 1994: 31-35
[11] Chan T F, Osher S, Shen J. The digital TV filter and nonlinear denoising [J]. IEEE Trans Imag. Proc. 2001, 10(2): 231-241