Discrete Total Variation: New Definition and Minimization

We propose a new definition for the gradient field of a discrete image, defined on a twice finer grid. The differentiation process from the image to its gradient field is viewed as the inverse operation of linear integration, and the proposed mapping is nonlinear. Then, we define the total variation of an image as the l1 norm of its gradient field amplitude. This new definition of the total variation yields sharp edges and has better isotropy than the classical definition.

[1]  Patrick L. Combettes,et al.  A forward-backward view of some primal-dual optimization methods in image recovery , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[2]  Michael K. Ng,et al.  Solving Constrained Total-variation Image Restoration and Reconstruction Problems via Alternating Direction Methods , 2010, SIAM J. Sci. Comput..

[3]  M. Hintermüller,et al.  Functional-analytic and numerical issues in splitting methods for total variation-based image reconstruction , 2014 .

[4]  A. Chambolle,et al.  An introduction to Total Variation for Image Analysis , 2009 .

[5]  F. Harlow,et al.  Numerical Calculation of Time‐Dependent Viscous Incompressible Flow of Fluid with Free Surface , 1965 .

[6]  Patrick L. Combettes,et al.  Image restoration subject to a total variation constraint , 2004, IEEE Transactions on Image Processing.

[7]  Laurent Condat,et al.  Joint demosaicking and denoising by total variation minimization , 2012, 2012 19th IEEE International Conference on Image Processing.

[8]  P. L. Combettes,et al.  Dualization of Signal Recovery Problems , 2009, 0907.0436.

[9]  Stanley Osher,et al.  A Guide to the TV Zoo , 2013 .

[10]  José M. Bioucas-Dias,et al.  Fast Image Recovery Using Variable Splitting and Constrained Optimization , 2009, IEEE Transactions on Image Processing.

[11]  Laurent Condat,et al.  A Fast Projection onto the Simplex and the l 1 Ball , 2015 .

[12]  Daniel Cremers,et al.  A Convex Approach to Minimal Partitions , 2012, SIAM J. Imaging Sci..

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

[14]  Pavel B. Bochev,et al.  Principles of Mimetic Discretizations of Differential Operators , 2006 .

[15]  Xavier Bresson,et al.  Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction , 2010, J. Sci. Comput..

[16]  Patrick L. Combettes,et al.  Proximal Algorithms for Multicomponent Image Recovery Problems , 2011, Journal of Mathematical Imaging and Vision.

[17]  P. L. Combettes,et al.  Proximity for sums of composite functions , 2010, 1007.3535.

[18]  A. Chambolle,et al.  Evolution of characteristic functions of convex sets in the plane by the minimizing total variation flow , 2005 .

[19]  Lionel Moisan,et al.  The Shannon Total Variation , 2017, Journal of Mathematical Imaging and Vision.

[20]  Laurent Condat,et al.  A Generic Proximal Algorithm for Convex Optimization—Application to Total Variation Minimization , 2014, IEEE Signal Processing Letters.

[21]  Patrick L. Combettes,et al.  A Monotone+Skew Splitting Model for Composite Monotone Inclusions in Duality , 2010, SIAM J. Optim..

[22]  Patrick L. Combettes,et al.  Proximal Splitting Methods in Signal Processing , 2009, Fixed-Point Algorithms for Inverse Problems in Science and Engineering.

[23]  Heinz H. Bauschke,et al.  Convex Analysis and Monotone Operator Theory in Hilbert Spaces , 2011, CMS Books in Mathematics.

[24]  Massimo Fornasier,et al.  Theoretical Foundations and Numerical Methods for Sparse Recovery , 2010, Radon Series on Computational and Applied Mathematics.

[25]  Sabine Süsstrunk,et al.  Linear demosaicing inspired by the human visual system , 2005, IEEE Transactions on Image Processing.

[26]  Camille Couprie,et al.  Dual Constrained TV-based Regularization on Graphs , 2013, SIAM J. Imaging Sci..

[27]  William K. Allard,et al.  Total Variation Regularization for Image Denoising, II. Examples , 2008, SIAM J. Imaging Sci..

[28]  Antonin Chambolle,et al.  An Upwind Finite-Difference Method for Total Variation-Based Image Smoothing , 2011, SIAM J. Imaging Sci..

[29]  W. Ring Structural Properties of Solutions to Total Variation Regularization Problems , 2000 .

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

[31]  Torsten Möller,et al.  Toward High-Quality Gradient Estimation on Regular Lattices , 2011, IEEE Transactions on Visualization and Computer Graphics.

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

[33]  Emanuele Schiavi,et al.  Box Relaxation Schemes in Staggered Discretizations for the Dual Formulation of Total Variation Minimization , 2013, IEEE Transactions on Image Processing.

[34]  Shiqian Ma,et al.  Alternating Proximal Gradient Method for Convex Minimization , 2016, J. Sci. Comput..

[35]  Mohamed-Jalal Fadili,et al.  Total Variation Projection With First Order Schemes , 2011, IEEE Transactions on Image Processing.

[36]  Laurent Condat,et al.  Gradient Estimation Revitalized , 2010, IEEE Transactions on Visualization and Computer Graphics.

[37]  Bang Công Vu,et al.  A splitting algorithm for dual monotone inclusions involving cocoercive operators , 2011, Advances in Computational Mathematics.

[38]  Wotao Yin,et al.  On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers , 2016, J. Sci. Comput..

[39]  Laurent Condat,et al.  A Primal–Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms , 2013, J. Optim. Theory Appl..

[40]  Laurent Condat Fast projection onto the simplex and the l1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pmb {l}_\mathbf {1}$$\end{ , 2015, Mathematical Programming.

[41]  Peter Blomgren,et al.  Mimetic finite difference methods in image processing , 2011 .

[42]  Stanley Osher,et al.  A Unified Primal-Dual Algorithm Framework Based on Bregman Iteration , 2010, J. Sci. Comput..

[43]  I. M. Otivation Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems , 2018 .