Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

Inverse Problems and Imaging

Volumn 7, Issue 4, 2013, Pages 1183-1214

  De Los Reyes, Juan Carlos ^a   Schönlieb, Carola Bibiane ^b  

^a ESCUELA POLITÉCNICA NACIONAL   (Ecuador)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Huber regularization; Image denoising; Noise distribution; PDE constrained optimization

Indexed keywords

EID: 84888390614     PISSN: 19308337     EISSN: 19308345     Source Type: Journal    
DOI: 10.3934/ipi.2013.7.1183     Document Type: Article

References (57)

1. R. Acar and C. R. Vogel, Analysis of bounded variation penalty methods for ill-posed problems, Inverse Problems, 10 (1994), 1217-1229.
2. A. Almansa, C. Ballester, V. Caselles and G. Haro, A TV based restoration model with local constraints, J. Sci. Comput., 34 (2008), 209-236.
3. G. Aubert and J.-F. Aujol, A variational approach to remove multiplicative noise, SIAM Journal on Applied Mathematics, 68 (2008), 925-946.
4. G. Aubert and L. Vese, A variational method in image recovery, SIAM J. Numer. Anal., 34 (1997), 1948-1979.
5. J.-F. Aujol and G. Gilboa, Constrained and SNR-based solutions for TV-Hilbert space image denoising, Journal of Mathematical Imaging and Vision, 26 (2006), 217-237.
6. F. Baus, M. Nikolova and G. Steidl, Smooth objectives composed of asymptotically affine data-fidelity and regularization: Bounds for the minimizers and parameter choice, Journal of Mathematical Imaging and Vision, Published Online (2013).
7. M. Bertalmio, V. Caselles, B. Rougé and A. Solé, TV based image restoration with local constraints, Journal of Scientific Computing, 19 (2003), 95-122.
8. A. Bovik, Handbook of Image and Video Processing, Academic Press, 2000.
9. G. Bouchitté, A. Braides and G. Buttazzo, Relaxation results for some free discontinuity problems, J. Reine Angew Math., 458 (1995), 1-18.
10. G. Bouchitté and G. Buttazzo, New lower semicontinuity results for nonconvex functionals defined on measures, Nonlinear Anal. TMA, 15 (1990), 679-692.
11. G. Bouchitté and G. Buttazzo, Relaxation for a class of nonconvex functionals defined on measures, Ann. Inst. H. Poincaré, 10 (1993), 345-361.
12. J. F. Cai, R. H. Chan and M. Nikolova, Two-phase approach for deblurring images corrupted by impulse plus Gaussian noise, Inverse Problems and Imaging, 2 (2008), 187-204.
13. E. Casas and L. Fernández, Distributed control of systems governed by a general class of quasilinear elliptic equations, J. Differential Equations, 104 (1993), 20-47.
14. A. Chambolle, An algorithm for total variation minimization and applications, J. Math. Imaging Vision, 20 (2004), 89-97.
15. A. Chambolle and P.-L. Lions, Image recovery via total variation minimization and related problems, Numer. Math., 76 (1997), 167-188.
16. A. Chambolle, V. Caselles, D. Cremers, M. Novaga and T. Pock, An Introduction to Total Variation for Image Analysis, Theoretical Foundations and Numerical Methods for Sparse Recovery (ed. M. Fornasier,), Radon Series on Computational and Applied Mathematics, De Gruyter Verlag, 263-340, 2010.
17. 1function approximation, SIAM J. Appl. Math., 65 (2005), 1817-1837.
18. T. F. Chan and J. J. Shen, Image Processing and Analysis-Variational, PDE, Wavelet, and Stochastic Methods, SIAM, (2005).
19. G. Dal Maso, An introduction to Gamma-convergence, Birkhäuser, Boston, 1993.
20. J. C. De los Reyes, Optimal control of a class of variational inequalities of the second kind, SIAM Journal on Control and Optimization, 49 (2011), 1629-1658.
21. J. C. De los Reyes, Optimization of mixed variational inequalities arising in ow of viscoplastic materials, Computational Optimization and Applications, 52 (2012), 757-784.
22. F. Demengel and R. Temam, Convex functions of a measure and applications, Indiana Univ. Math. J., 33 (1984), 673-709.
23. D. C. Dobson and C. R. Vogel, Convergence of an iterative method for total variation denoising, SIAM J. Numer. Anal., 34 (1997), 1779-1791.
24. J. Domke, Generic methods for optimization-based modeling, in International Conference on Artificial Intelligence and Statistics, 318-326, 2012.
25. J. Domke, Learning graphical model parameters with approximate marginal inference, Pub-lished Online, PAMI, (2013).
26. V. Duval, J.-F. Aujol and Y. Gousseau, The TVL1 model: A geometric point of view, SIAM Journal on Multiscale Modeling and Simulation, 8 (2009), 154-189.
27. M. Fornasier, V. Naumova and S. V. Pereverzyev, Parameter Choice Strategies for Multi-Penalty Regularization, preprint, July 2013. http://www-m15.ma.tum.de/foswiki/pub/M15/ Allgemeines/PublicationsEN/parameterfichoiceástrategies.pdf.
28. K. Frick, P. Marnitz and A. Munk, Statistical multiresolution dantzig estimation in imaging: Fundamental concepts and algorithmic framework, Electron. J. Stat., 6 (2012), 231-268.
29. K. Frick, P. Marnitz and A. Munk, Shape constrained regularization by statistical multiresolution for inverse problems, Inverse Problems, 28 (2012), 065006.
30. K. Frick, P. Marnitz and A. Munk, Statistical multiresolution estimation for variational imaging: With an application in poisson-biophotonics, Journal of Mathematical Imaging and Vi-sion, 46 (2013), 370-387.
31. P. Getreuer, M. Tong and L. A. Vese, A variational model for the restoration of MR images corrupted by blur and Rician noise, in Advances in Visual Computing, Springer Berlin-Heidelberg-Chicago, 686-698, 2012.
32. G. Gilboa, N. Sochen and Y. Y. Zeevi, Texture preserving variational denoising using an adaptive fidelity term, in Proc. VLsM, (Vol. 3), 2003.
33. G. Gilboa, N. Sochen and Y. Y. Zeevi, Estimation of optimal PDE-based denoising in the SNR sense, Image Processing, IEEE Transactions on, 15 (2006), 2269-2280.
34. M. Hintermüller, Y. Dong and M. M. Rincon-Camacho, Automated regularization parameter selection in multi-scale total variation models for image restoration, Journal of Mathematical Imaging and Vision, 40 (2011), 82-104.
35. M. Hintermüller and K. Kunisch Total bounded variation regularization as a bilaterally constrained optimization problem, SIAM Journal on Applied Mathematics, 64 2004, 1311-1333.
36. M. Hintermüller and A. Langer, Subspace Correction Methods for a Class of Non-Smooth and Non-Additive Convex Variational Problems in Image Processing, Accepted by SIAM J. Imaging Sciences, 2013, 34 pp. http://math.uni-graz.at/mobis/publications/ SFB-Report-2012-021.pdf.
37. M. Hintermüller and G. Stadler, An infeasible primal-dual algorithm for total bounded variation-based inf-convolution-type image restoration, SIAM Journal on Scientific Com-puting, 28 (2006), 1-23.
38. J. M. Chung, M. Chung and D. P. O'Leary, Designing optimal spectral filters for inverse problems, SIAM Journal on Scientific Computing, 33 (2011), 3132-3152.
39. T. Le, R. Chartrand and T. J. Asaki, A variational approach to reconstructing images corrupted by Poisson noise, J. Math. Imaging Vision, 27 (2007), 257-263.
40. Risheng Liu, Zhouchen Lin, Wei Zhang and Zhixun Su, Learning PDEs for Image Restoration via Optimal Control, ECCV 2010.
41. K. Kunisch and T. Pock, A bilevel optimization approach for parameter learning in variational models, SIAM Journal on Imaging Sciences, 6 (2013), 938-983.
42. B. Manz et al., L. F. Gladden and P. B. Warren, Flow and dispersion in porous media: Lattice-Boltzmann and NMR studies, AIChE Journal, 45 (1999), 1845-1854.
43. M. D. Mantle et al., A. J. Sederman and L. F. Gladden, Single-and two-phase ow in fixed-bed reactors: MRI ow visualisation and lattice-Boltzmann simulations, Chemical Engineering Science, 56 (2001), 523-529.
44. V. A. Morozov, Regularization Methods for Ill-posed Problems, CRC Press, Boca Raton, 1993.
45. M. Nikolova, A variational approach to remove outliers and impulse noise, J. Math. Imaging Vision, 20 (2004), 99-120.
46. G. Peyré and J. Fadili, Learning analysis sparsity priors, Proc. of Sampta'11, (2011).
47. A. Sawatzky, C. Brune, J. Müller and M. Burger, Total variation processing of images with poisson statistics, Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns, 5702 (2009), 533-540.
48. F. R. Schmidt and D. Cremers, A closed-form solution for image sequence segmentation with dynamical shape priors, in Pattern Recognition (Proc. DAGM), 2009.
49. D. Strong, J.-F. Aujol and T. Chan, Scale recognition, regularization parameter selection, and Meyers G norm in total variation regularization, SIAM Journal on Multiscale Modeling and Simulation, 5 (2006), 273-303.
50. S. P. Sullivan et al., F. M. Sani, M. L. Johns and L. F. Gladden, Simulation of packed bed reactors using lattice Boltzmann methods, Chemical Engineering Science, 60 (2005), 3405-3418.
51. M. F. Tappen, Utilizing variational optimization to learn Markov random fields, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR2007), 2007.
52. M. F. Tappen, C. Liu, E. H. Adelson and W. T. Freeman, Learning Gaussian conditional random fields for low-level vision, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR2007), 2007.
53. I. Tosic, I. Jovanovic, P. Frossard, M. Vetterli and N. Duric, Ultrasound tomography with learned dictionaries, IEEE International Conference on Acoustics, Speech, and Signal Pro-cessing, Dallas, Texas, International Conference on Acoustics Speech and Signal Processing ICASSP, 2010.
54. L. Vese, A study in the BV space of a denoising-deblurring variational problem, Appl. Math. Optim., 44 (2001), 131-161.
55. C. R. Vogel, Computational Methods for Inverse Problems, SIAM, vol. 10, 2002.
56. C. R. Vogel and M. E. Oman, Iterative methods for total variation denoising, SIAM J. Sci. Comput., 17 (1996), 227-238, Special Issue on Iterative Methods in Numerical Linear Algebra (Breckenridge, CO, 1994).
57. A. M. Yip and F. Park, Solution Dynamics, Causality, and Critical Behavior of the Regular-ization Parameter in Total Variation Denoising Problems, CAM reports 03-59, 2003.