A symmetric alternating minimization algorithm for total variation minimization

[1]  Chuan-pei Xu,et al.  A primal-dual multiplier method for total variation image restoration , 2019, Applied Numerical Mathematics.

[2]  R. Barrio,et al.  Inertial Nonconvex Alternating Minimizations for the Image Deblurring , 2019, IEEE Transactions on Image Processing.

[3]  Alexander Gasnikov,et al.  Accelerated Alternating Minimization , 2019, ArXiv.

[4]  S. Guminov,et al.  Accelerated Alternating Minimization, Accelerated Sinkhorn's Algorithm and Accelerated Iterative Bregman Projections. , 2019 .

[5]  Hongwei Liu,et al.  Accelerated augmented Lagrangian method for total variation minimization , 2019, Comput. Appl. Math..

[6]  Leopold Matamba Messi,et al.  Galerkin method with splines for total variation minimization , 2019, Journal of Algorithms & Computational Technology.

[7]  Xiaoming Yuan,et al.  An accelerated primal-dual iterative scheme for the L2-TV regularized model of linear inverse problems , 2019, Inverse Problems.

[8]  Yunhai Xiao,et al.  An efficient algorithm for batch images alignment with adaptive rank-correction term , 2019, J. Comput. Appl. Math..

[9]  Sylvain Sardy,et al.  Efficient Threshold Selection for Multivariate Total Variation Denoising , 2018, Journal of Computational and Graphical Statistics.

[10]  Roland Herzog,et al.  Discrete Total Variation with Finite Elements and Applications to Imaging , 2018, Journal of Mathematical Imaging and Vision.

[11]  Jiaxin Xie,et al.  On inexact ADMMs with relative error criteria , 2018, Comput. Optim. Appl..

[12]  Michael K. Ng,et al.  A Fast Algorithm for Deconvolution and Poisson Noise Removal , 2018, J. Sci. Comput..

[13]  R. Herzog,et al.  Discrete Total Variation with Finite Elements and Applications to Imaging , 2018, Journal of Mathematical Imaging and Vision.

[14]  Yuan Lei,et al.  A new accelerated alternating minimization method for analysis sparse recovery , 2018, Signal Process..

[15]  Fengmin Xu,et al.  A novel method for a class of structured low-rank minimizations with equality constraint , 2018, J. Comput. Appl. Math..

[16]  Yunhai Xiao,et al.  Symmetric Gauss–Seidel Technique-Based Alternating Direction Methods of Multipliers for Transform Invariant Low-Rank Textures Problem , 2017, Journal of Mathematical Imaging and Vision.

[17]  Yang Wang,et al.  Fast Rank-One Alternating Minimization Algorithm for Phase Retrieval , 2017, Journal of Scientific Computing.

[18]  Jian-Feng Cai,et al.  Spectral Compressed Sensing via Projected Gradient Descent , 2017, SIAM J. Optim..

[19]  Yurii Nesterov,et al.  Efficiency of the Accelerated Coordinate Descent Method on Structured Optimization Problems , 2017, SIAM J. Optim..

[20]  Kim-Chuan Toh,et al.  QSDPNAL: a two-phase augmented Lagrangian method for convex quadratic semidefinite programming , 2015, Mathematical Programming Computation.

[21]  Kim-Chuan Toh,et al.  An efficient inexact symmetric Gauss–Seidel based majorized ADMM for high-dimensional convex composite conic programming , 2015, Mathematical Programming.

[22]  Kim-Chuan Toh,et al.  An Efficient Inexact ABCD Method for Least Squares Semidefinite Programming , 2015, SIAM J. Optim..

[23]  Amir Beck,et al.  On the Convergence of Alternating Minimization for Convex Programming with Applications to Iteratively Reweighted Least Squares and Decomposition Schemes , 2015, SIAM J. Optim..

[24]  James M. Ortega,et al.  Iterative solution of nonlinear equations in several variables , 2014, Computer science and applied mathematics.

[25]  Ting Kei Pong,et al.  Penalty Methods for a Class of Non-Lipschitz Optimization Problems , 2014, SIAM J. Optim..

[26]  Kim-Chuan Toh,et al.  A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions , 2014, Mathematical Programming.

[27]  Lixin Shen,et al.  The moreau envelope approach for the l1/TV image denoising model , 2014 .

[28]  Marco Donatelli,et al.  A fast alternating minimization algorithm for total variation deblurring without boundary artifacts , 2013, 1308.6754.

[29]  Yonina C. Eldar,et al.  Smoothing and Decomposition for Analysis Sparse Recovery , 2013, IEEE Transactions on Signal Processing.

[30]  Yin Zhang,et al.  An efficient augmented Lagrangian method with applications to total variation minimization , 2013, Computational Optimization and Applications.

[31]  Raymond H. Chan,et al.  Constrained Total Variation Deblurring Models and Fast Algorithms Based on Alternating Direction Method of Multipliers , 2013, SIAM J. Imaging Sci..

[32]  Ambuj Tewari,et al.  On the Nonasymptotic Convergence of Cyclic Coordinate Descent Methods , 2013, SIAM J. Optim..

[33]  K. Toh,et al.  An Inexact Accelerated Proximal Gradient Method for Large Scale Linearly Constrained Convex SDP , 2012, SIAM J. Optim..

[34]  Xiaoming Yuan,et al.  Alternating algorithms for total variation image reconstruction from random projections , 2012 .

[35]  Marc Teboulle,et al.  Smoothing and First Order Methods: A Unified Framework , 2012, SIAM J. Optim..

[36]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[37]  I. Loris,et al.  On a generalization of the iterative soft-thresholding algorithm for the case of non-separable penalty , 2011, 1104.1087.

[38]  Xuecheng Tai,et al.  AUGMENTED LAGRANGIAN METHOD FOR TOTAL VARIATION RESTORATION WITH NON-QUADRATIC FIDELITY , 2011 .

[39]  Tony F. Chan,et al.  A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science , 2010, SIAM J. Imaging Sci..

[40]  Zhaosong Lu,et al.  Penalty decomposition methods for rank minimization , 2010, Optim. Methods Softw..

[41]  Xue-Cheng Tai,et al.  Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models , 2010, SIAM J. Imaging Sci..

[42]  José M. Bioucas-Dias,et al.  An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems , 2009, IEEE Transactions on Image Processing.

[43]  Yin Zhang,et al.  Alternating Direction Algorithms for 1-Problems in Compressive Sensing , 2009, SIAM J. Sci. Comput..

[44]  A. Beck,et al.  Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.

[45]  Junfeng Yang,et al.  An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise , 2009, SIAM J. Sci. Comput..

[46]  Emmanuel J. Candès,et al.  NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..

[47]  Junfeng Yang,et al.  A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration , 2009, SIAM J. Imaging Sci..

[48]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[49]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..

[50]  Stephen J. Wright,et al.  Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.

[51]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[52]  José M. Bioucas-Dias,et al.  A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.

[53]  Stephen P. Boyd,et al.  An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares , 2007, IEEE Journal of Selected Topics in Signal Processing.

[54]  Mila Nikolova,et al.  Efficient Minimization Methods of Mixed l2-l1 and l1-l1 Norms for Image Restoration , 2005, SIAM J. Sci. Comput..

[55]  Richard G. Baraniuk,et al.  ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems , 2004, IEEE Transactions on Signal Processing.

[56]  T. Chan,et al.  On the Convergence of the Lagged Diffusivity Fixed Point Method in Total Variation Image Restoration , 1999 .

[57]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[58]  Vladimir G. Spokoiny,et al.  Convergence of an Alternating Maximization Procedure , 2016, J. Mach. Learn. Res..

[59]  A. Chambolle,et al.  A remark on accelerated block coordinate descent for computing the proximity operators of a sum of convex functions , 2015 .

[60]  Otmar Scherzer,et al.  Handbook of Mathematical Methods in Imaging , 2015, Handbook of Mathematical Methods in Imaging.

[61]  Xue-Cheng Tai,et al.  Augmented Lagrangian Method for Total Variation Based Image Restoration and Segmentation Over Triangulated Surfaces , 2012, J. Sci. Comput..

[62]  Gitta Kutyniok.  Compressed Sensing , 2012 .

[63]  J. Koko,et al.  An Augmented Lagrangian Method for , 2010 .

[64]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[65]  Curtis R. Vogel,et al.  Iterative Methods for Total Variation Denoising , 1996, SIAM J. Sci. Comput..

[66]  Curtis R. Vogel,et al.  Ieee Transactions on Image Processing Fast, Robust Total Variation{based Reconstruction of Noisy, Blurred Images , 2022 .