[1] [1] Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D，1992，60: 259-268.

[2] [2] Lysaker M， Lundervold A， Tai X C. Noise removal using fourth order partial differential equation with applications to medical magnetic resonance images in space and time[J]. IEEE Transactions on Image Processing， 2003， 12(12): 1579-1590.

[3] [3] Bredies K， Kunisch K， Pock T. Total generalized variation[J]. SIAM Journal on Imaging Sciences，2010，3(3): 492-526.

[4] [4] Chambolle A， Pock T. A first-order primal-dual algorithm for convex problems with applications to imaging[J]. Journal Mathematical Imaging Vision， 2011， 40(1): 120-145.

[5] [5] Chambolle A， Lions PL. Image recovery via total variation minimization and related problems[J]. Numerische Mathematik，1997，76(2): 167-188.

[6] [6] Weickert J. Theoretical foundation of anisotropic diffusion in image processing[J]. Computing Supplement，1996，11: 221-236.

[7] [7] Bredies K，Valkonen T. Inverse problems with second-order total generalized variation constraints[A]. Proceedings of SampTA 2011-9th International conference on sampling theory and applications， Singapore，2011：1-4.

[8] [8] Knoll F， Bredies K， Pock T， et al. Second order total generalized variation (TGV) for MRI[J]. Magnetic Resonance in Medicine，2011，65(2): 480-491.

[9] [9] Pock T，Zebedin L，Bischof H. TGV-Fusion [J]. Springer-Verlag，2011，6570: 245-258.

[10] [10] Kirsch R. Computer detection of the constituent structure of biological image[J]. Computers and Biomedical Research，1971，4(3): 315-328.