Abstract

We analyze a new notion of total anisotropic higher-order variation which, differently from total generalized variation in [K. Bredies, K. Kunisch, and T. Pock, SIAM J. Imaging Sci., 3 (2010), pp. 492--526], quantifies for possibly nonsymmetric tensor fields their variations at arbitrary order weighted by possibly inhomogeneous, smooth elliptic anisotropies. We prove some properties of this total variation and of the associated spaces of tensors with finite variations. We show the existence of solutions to a related regularity-fidelity optimization problem. We also prove a decomposition formula which appears to be helpful for the design of numerical schemes, as shown in a companion paper, where several applications to image processing are studied.

Keywords

  1. anisotropic total variation
  2. higher-order total variation
  3. variational model

MSC codes

  1. 47A52
  2. 49M30
  3. 49N45
  4. 65J22
  5. 94A08

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References

1.
M. Amar and G. Bellettini, A notion of total variation depending on a metric with discontinuous coefficients, Ann. Inst. H. Poincaré Anal. Non Linéaire, 11 (1994), pp. 91--133, https://doi.org/10.1016/S0294-1449(16)30197-4.
2.
L. Ambrosio, N. Fusco, and D. Pallara, Functions of Bounded Variation and Free Discontinuity Problems, Oxford Mathematical Monographs, Clarendon Press, Oxford University Press, New York, 2000.
3.
H. Attouch and H. Brezis, Duality for the sum of convex functions in general Banach spaces, Asp. Math. and Appl., 34 (1986), pp. 125--133, https://doi.org/10.1016/S0924-6509(09)70252-1.
4.
I. Bayram and M. E. Kamasak, Directional total variation, IEEE Signal Process. Lett., 19 (2012), pp. 781--784, https://doi.org/10.1109/LSP.2012.2220349.
5.
B. Berkels, M. Burger, M. Droske, O. Nemitz, and M. Rumpf, Cartoon extraction based on anisotropic image classification, in Vision, Modeling, and Visualization Proceedings, IOS Press, Amsterdam, Netherlands, 2006, pp. 293--300, http://numod.ins.uni-bonn.de/research/papers/public/BeBuDr06.pdf.
6.
J. Borwein and J. Vanderwerff, Convex Functions: Constructions, Characterizations and Counterexamples, Encyclopedia Math. Appl., Cambridge University Press, Cambridge, 2010.
7.
K. Bredies, Symmetric tensor fields of bounded deformation, Ann. Mat. Pura Appl. (4), 192 (2013), pp. 815--851, https://doi.org/10.1007/s10231-011-0248-4.
8.
K. Bredies and M. Holler, Regularization of linear inverse problems with total generalized variation, J. Inverse Ill-Posed Probl., 22 (2014), pp. 871--913, https://doi.org/10.1515/jip-2013-0068.
9.
K. Bredies, K. Kunisch, and T. Pock, Total generalized variation, SIAM J. Imaging Sci., 3 (2010), pp. 492--526, https://doi.org/10.1137/090769521.
10.
H. Brezis, Functional Analysis, Sobolev Spaces and Partial Differential Equations, Universitext, Springer, New York, 2010, https://doi.org/10.1007/978-0-387-70914-7.
11.
V. Caselles, A. Chambolle, D. Cremers, M. Novaga, and T. Pock, An introduction to total variation for image analysis, Theoret. Found. Numer. Methods Sparse Recovery, 9 (2010), pp. 263--340, https://doi.org/10.1515/9783110226157.263.
12.
T. Chan, S. Esedoglu, and F. Park, A fourth order dual method for staircase reduction in texture extraction and image restoration problems, in 2010 IEEE International Conference on Image Processing, IEEE, Piscataway, NJ, 2010, pp. 4137--4140, https://doi.org/10.1109/ICIP.2010.5653199.
13.
T. Chan, A. Marquina, and P. Mulet, High-order total variation-based image restoration, SIAM J. Sci. Comput., 22 (2000), pp. 503--516, https://doi.org/10.1137/S1064827598344169.
14.
R. Dalgas Kongskov, Y. Dong, and K. Knudsen, Directional Total Generalized Variation Regularization, arXiv e-prints, https://arxiv.org/abs/1701.02675 (2017).
15.
Y. Dong and M. Hintermüller, Multi-Scale Total Variation with Automated Regularization Parameter Selection for Color Image Restoration, Springer, New York, 2009, pp. 271--281, https://doi.org/10.1007/978-3-642-02256-2_23.
16.
M. J. Ehrhardt and M. M. Betcke, Multicontrast MRI reconstruction with structure-guided total variation, SIAM J. Imaging Sci., 9 (2016), pp. 1084--1106, https://doi.org/10.1137/15M1047325.
17.
V. Estellers, S. Soatto, and X. Bresson, Adaptive regularization with the structure tensor, IEEE Trans. Image Process., 24 (2015), pp. 1777--1790, https://doi.org/10.1109/TIP.2015.2409562.
18.
M. Grasmair and F. Lenzen, Anisotropic total variation filtering, Appl. Math. Optim., 62 (2010), pp. 323--339, https://doi.org/10.1007/s00245-010-9105-x.
19.
S. Lefkimmiatis, A. Roussos, P. Maragos, and M. Unser, Structure tensor total variation, SIAM J. Imaging Sci., 8 (2015), pp. 1090--1122, https://doi.org/10.1137/14098154X.
20.
F. Lenzen, F. Becker, J. Lellmann, S. Petra, and C. Schnörr, A class of quasi-variational inequalities for adaptive image denoising and decomposition, Comput. Optim. Appl., 54 (2013), pp. 371--398, https://doi.org/10.1007/s10589-012-9456-0.
21.
K. Papafitsoros and C. B. Schönlieb, A combined first and second order variational approach for image reconstruction, J. Math. Imaging Vision, 48 (2014), pp. 308--338, https://doi.org/10.1007/s10851-013-0445-4.
22.
S. Parisotto, J. Lellmann, S. Masnou, and C. B. Schönlieb, Higher Order Total Directional Variation: Imaging Applications, arXiv e-prints, https://arxiv.org/abs/1812.05023, 2018.
23.
L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Phys. D, 60 (1992), pp. 259--268, https://doi.org/10.1016/0167-2789(92)90242-F.
24.
S. Setzer and G. Steidl, Variational methods with higher-order derivatives in image processing, Approximation XII, (2008), pp. 360--386.
25.
G. Steidl and T. Teuber, Anisotropic Smoothing Using Double Orientations, Springer, New York, 2009, pp. 477--489, https://doi.org/10.1007/978-3-642-02256-2_40.
26.
C. Wu and X.-C. Tai, Augmented Lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models, SIAM J. Imaging Sci., 3 (2010), pp. 300--339, https://doi.org/10.1137/090767558.
27.
H. Zhang and Y. Wang, Edge adaptive directional total variation, J. Engrg., (2013), https://doi.org/10.1049/joe.2013.0116.

Information & Authors

Information

Published In

cover image SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences
Pages: 474 - 496
ISSN (online): 1936-4954

History

Submitted: 17 January 2019
Accepted: 6 January 2020
Published online: 24 March 2020

Keywords

  1. anisotropic total variation
  2. higher-order total variation
  3. variational model

MSC codes

  1. 47A52
  2. 49M30
  3. 49N45
  4. 65J22
  5. 94A08

Authors

Affiliations

Carola-Bibiane Schönlieb

Funding Information

Agence Nationale de la Recherche https://doi.org/10.13039/501100001665 : ANR-14-CE27-0019

Funding Information

Alan Turing Institute https://doi.org/10.13039/100012338 : TU/B/000071

Funding Information

H2020 Marie Skłodowska-Curie Actions https://doi.org/10.13039/100010665 : 777826

Funding Information

Horizon 2020 Framework Programme https://doi.org/10.13039/100010661

Funding Information

Isaac Newton Institute for Mathematical Sciences https://doi.org/10.13039/100012112

Funding Information

Leverhulme Trust https://doi.org/10.13039/501100000275

Funding Information

Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 : EP/L016516/1

Funding Information

Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 : EP/M00483X/1, EP/K009745/1, EP/N014588/1

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