| [1] Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM J Imaging Sci, 2008, 2(1):183-202.[2] Chambolle A, DeVore R, Lee N, Lucier B. Nonlinear wavelet image processing:Variational problems, compression, and noise removal through wavelet shrinkage[J]. IEEE Trans Imag Proc, 1998, 7:319-335.[3] Chambolle A, Pock T. A first-order primal-dual algorithms for convex problem with applications to imaging[J]. J Math Imaging Vis 2011, 40(1):120-145.[4] He B S, Yuan X M. Convergence analysis of primal-dual algorithms for a saddle-point problem:From contraction perspective[J]. SIAM J Imaging Sci, 2012, 5:119-149.[5] Donoho D. Compressed sensing[J]. IEEE Trans. Inform. Theory, 2006, 52:1289-1306.[6] Esser E, Zhang X, Chan T F. A general framework for a class of first-order primal-dual algorithms for TV minimization[J]. SIAM J Imaging Sci, 2010, 3:1015-1046.[7] Yu G H, Qi L Q, Dai Y H. On nonmonotone Chambolle gradient projection algorithms for total variation image restoration[J]. J Math Imaging Vis, 2009, 35:143-154.[8] Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D, 1992, 60:259-268.[9] Figueiredo M, Nowak R. An EM algorithm for wavelet-based image restoration[J]. IEEE Tran Image Process, 2003, 12:906-916.[10] Zhu M Q, Wright S J, Chan T F. Duality-based algorithms for total variation regularized image restoration[J]. Comput Optim Appl, 2008, 47:377-400.[11] Zhu M Q, Chan T F. An efficient primal-dual hybrid gradient algorithm for total variation image restoration[R]. UCLA CAM Reports, 2008, 08-34.[12] Combettes P, Wajs W. Signal recovery by proximal forward-backward splitting[J]. Multiscale Model Sim, 2006, 4(4):1168-1200.[13] Rockafellar R. Monotone operators and the proximal point algorithm[J]. SIAM J Control Optim, 1976, 14:877-898.[14] Cai X J, Han D R, Xu L L. An improved first-order primal-dual algorithm with a new correction step[J]. J Global Optim, 2013, 57(4):1419-1428.[15] He B S. PPA-like contraction methods for convex optimization:A framework using variational inequality approach[J]. J Oper Res Soc China, 2016, 3(4):391-420.[16] Yin W T, Osher S, Goldfarb D, Darbon J. Bregman iterative algorithms forl1-minimization with applications to compressed sensing[J]. SIAM J Imaging Sci, 2008, 1:143-168.[17] Cai J F, Osher S, Shen Z W. Linearized bregman iterations for compressed sensing[J]. Math Comput, 2009, 78(267):1515-1536.[18] Wright S J, Nowak R D, Figueiredo M A T. Sparse reconstruction by separable approximation[J]. IEEE Trans Signal Proc, 2009, 57(7):2479-2493.[19] Shen Y, Wang C J. A self-adaptive projection and contraction method for optimizing the sum of a convex and a quadratic function[J]. Pacific J Optim, 2013, 9(1):137-154. |
