American Institute of Mathematical Sciences

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May  2013, 7(2): 397-416. doi: 10.3934/ipi.2013.7.397

Gaussian Markov random field priors for inverse problems

Johnathan M. Bardsley 1,

1. 

Department of Mathematical Sciences, University of Montana, Missoula, Montana 59812, United States

Received  January 2012 Revised  March 2013 Published  May 2013

In this paper, our focus is on the connections between the methods of (quadratic) regularization for inverse problems and Gaussian Markov random field (GMRF) priors for problems in spatial statistics. We begin with the most standard GMRFs defined on a uniform computational grid, which correspond to the oft-used discrete negative-Laplacian regularization matrix. Next, we present a class of GMRFs that allow for the formation of edges in reconstructed images, and then draw concrete connections between these GMRFs and numerical discretizations of more general diffusion operators. The benefit of the GMRF interpretation of quadratic regularization is that a GMRF is built-up from concrete statistical assumptions about the values of the unknown at each pixel given the values of its neighbors. Thus the regularization term corresponds to a concrete spatial statistical model for the unknown, encapsulated in the prior. Throughout our discussion, strong ties between specific GMRFs, numerical discretizations of diffusion operators, and corresponding regularization matrices, are established. We then show how such GMRF priors can be used for edge-preserving reconstruction of images, in both image deblurring and medical imaging test cases. Moreover, we demonstrate the effectiveness of GMRF priors for data arising from both Gaussian and Poisson noise models.
Keywords: Inverse problems, image reconstruction., Gaussian Markov random fields, Bayesian inference, regularization, numerical partial differential equations.
Mathematics Subject Classification: Primary: 15A29, 65F22, 65C60; Secondary: 94A0.
Citation: Johnathan M. Bardsley. Gaussian Markov random field priors for inverse problems. Inverse Problems & Imaging, 2013, 7 (2) : 397-416. doi: 10.3934/ipi.2013.7.397
References:
[1]

J. M. Bardsley, D. Calvetti and E. Somersalo, Hierarchical regularization for edge-preserving reconstruction of PET images, Inverse Problems, 26 (2010), 035010. doi: 10.1088/0266-5611/26/3/035010.  Google Scholar

[2]

J. M. Bardsley and J. Goldes, An iterative method for edge-preserving MAP estimation when data-noise is poisson, SIAM J. Sci. Comput., 32 (2010), 171-185. doi: 10.1137/080726884.  Google Scholar

[3]

J. M. Bardsley and J. Goldes, Regularization parameter selection methods for ill-posed poisson maximum likelihood estimation, Inverse Problems, 25 (2009), 095005. doi: 10.1088/0266-5611/25/9/095005.  Google Scholar

[4]

J. M. Bardsley and C. R. Vogel, A nonnnegatively constrained convex programming method for image reconstruction, SIAM J. Sci. Comput., 25 (2004), 1326-1343. doi: 10.1137/S1064827502410451.  Google Scholar

[5]

J. Besag, Spatial interaction and the statistical analysis of lattice systems, J. R. Stat. Soc. Ser. B, 36 (1974), 192-236.  Google Scholar

[6]

D. Calvetti and E. Somersalo, "Introduction to Bayesian Scientific Computing," Springer, New York, 2007.  Google Scholar

[7]

D. Calvetti and E. Somersalo, A Gaussian hypermodel to recover blocky objects, Inverse Problems, 23 (2007), 733-754. doi: 10.1088/0266-5611/23/2/016.  Google Scholar

[8]

D. Calvetti and E. Somersalo, Hypermodels in the bayesian imaging framework, Inverse Problems, 24 (2008), 034013. doi: 10.1088/0266-5611/24/3/034013.  Google Scholar

[9]

D. Calvetti, J. Kaipio and E. Somersalo, Aristotelian prior boundary conditions, Intl. J. Math. Comp. Sci., 1 (2005), 63-81.  Google Scholar

[10]

H. K. Engl, M. Hanke and A. Neubauer, "Regularization of Inverse Problems," Kluwer, The Netherlands, 1996.  Google Scholar

[11]

P. C. Hansen, "Discrete Inverse Problems: Insight and Algorithms," SIAM, Philadelphia, 2010. doi: 10.1137/1.9780898718836.  Google Scholar

[12]

J. Kaipio, V. Kolehmainen, M. Vauhkonen and E. Somersalo, Inverse problems with structural prior information, Inverse Problems, 15 (1999), 713-729. doi: 10.1088/0266-5611/15/3/306.  Google Scholar

[13]

J. Kaipio and E. Somersalo, "Statistical and Computational Inverse Problems," Springer, New York, 2005.  Google Scholar

[14]

V. Kolehmainen, T. Tarvainen, S. R. Arridge and J. P. Kaipio, Marginalization of uninteresting distributed parameters in inverse problems-application to diffuse optical tomography, Intl. J. Uncertainty Quantification, 1 (2011), 1-17. doi: 10.1615/Int.J.UncertaintyQuantification.v1.i1.10.  Google Scholar

[15]

F. Natterer and F. Wübbeling, "Mathematical Methods in Image Reconstruction," SIAM, Philadelphia, 2001. doi: 10.1137/1.9780898718324.  Google Scholar

[16]

J. M. Ollinger and J. A. Fessler, Positron-emission tomography, IEEE Signal Processing Magazine, (1997), 43-55. doi: 10.1109/79.560323.  Google Scholar

[17]

H. Rue and L. Held, "Gaussian Markov Random Fields: Theory and Applications," Chapman and Hall/CRC, Boca Raton, FL, 2005. doi: 10.1201/9780203492024.  Google Scholar

[18]

C. R. Vogel, "Computational Methods for Inverse Problems," SIAM, Philadelphia, 2002. doi: 10.1137/1.9780898717570.  Google Scholar

[19]

C. R. Vogel and M. E. Oman, A fast, robust algorithm for total variation based reconstruction of noisy, blurred images, IEEE Trans. Image Process., 7 (1998), 813-824. doi: 10.1109/83.679423.  Google Scholar

[20]

D. Watkins, "Fundamentals of Matrix Computations," Wiley, Hoboken, NJ, 2010.  Google Scholar

show all references

References:
[1]

J. M. Bardsley, D. Calvetti and E. Somersalo, Hierarchical regularization for edge-preserving reconstruction of PET images, Inverse Problems, 26 (2010), 035010. doi: 10.1088/0266-5611/26/3/035010.  Google Scholar

[2]

J. M. Bardsley and J. Goldes, An iterative method for edge-preserving MAP estimation when data-noise is poisson, SIAM J. Sci. Comput., 32 (2010), 171-185. doi: 10.1137/080726884.  Google Scholar

[3]

J. M. Bardsley and J. Goldes, Regularization parameter selection methods for ill-posed poisson maximum likelihood estimation, Inverse Problems, 25 (2009), 095005. doi: 10.1088/0266-5611/25/9/095005.  Google Scholar

[4]

J. M. Bardsley and C. R. Vogel, A nonnnegatively constrained convex programming method for image reconstruction, SIAM J. Sci. Comput., 25 (2004), 1326-1343. doi: 10.1137/S1064827502410451.  Google Scholar

[5]

J. Besag, Spatial interaction and the statistical analysis of lattice systems, J. R. Stat. Soc. Ser. B, 36 (1974), 192-236.  Google Scholar

[6]

D. Calvetti and E. Somersalo, "Introduction to Bayesian Scientific Computing," Springer, New York, 2007.  Google Scholar

[7]

D. Calvetti and E. Somersalo, A Gaussian hypermodel to recover blocky objects, Inverse Problems, 23 (2007), 733-754. doi: 10.1088/0266-5611/23/2/016.  Google Scholar

[8]

D. Calvetti and E. Somersalo, Hypermodels in the bayesian imaging framework, Inverse Problems, 24 (2008), 034013. doi: 10.1088/0266-5611/24/3/034013.  Google Scholar

[9]

D. Calvetti, J. Kaipio and E. Somersalo, Aristotelian prior boundary conditions, Intl. J. Math. Comp. Sci., 1 (2005), 63-81.  Google Scholar

[10]

H. K. Engl, M. Hanke and A. Neubauer, "Regularization of Inverse Problems," Kluwer, The Netherlands, 1996.  Google Scholar

[11]

P. C. Hansen, "Discrete Inverse Problems: Insight and Algorithms," SIAM, Philadelphia, 2010. doi: 10.1137/1.9780898718836.  Google Scholar

[12]

J. Kaipio, V. Kolehmainen, M. Vauhkonen and E. Somersalo, Inverse problems with structural prior information, Inverse Problems, 15 (1999), 713-729. doi: 10.1088/0266-5611/15/3/306.  Google Scholar

[13]

J. Kaipio and E. Somersalo, "Statistical and Computational Inverse Problems," Springer, New York, 2005.  Google Scholar

[14]

V. Kolehmainen, T. Tarvainen, S. R. Arridge and J. P. Kaipio, Marginalization of uninteresting distributed parameters in inverse problems-application to diffuse optical tomography, Intl. J. Uncertainty Quantification, 1 (2011), 1-17. doi: 10.1615/Int.J.UncertaintyQuantification.v1.i1.10.  Google Scholar

[15]

F. Natterer and F. Wübbeling, "Mathematical Methods in Image Reconstruction," SIAM, Philadelphia, 2001. doi: 10.1137/1.9780898718324.  Google Scholar

[16]

J. M. Ollinger and J. A. Fessler, Positron-emission tomography, IEEE Signal Processing Magazine, (1997), 43-55. doi: 10.1109/79.560323.  Google Scholar

[17]

H. Rue and L. Held, "Gaussian Markov Random Fields: Theory and Applications," Chapman and Hall/CRC, Boca Raton, FL, 2005. doi: 10.1201/9780203492024.  Google Scholar

[18]

C. R. Vogel, "Computational Methods for Inverse Problems," SIAM, Philadelphia, 2002. doi: 10.1137/1.9780898717570.  Google Scholar

[19]

C. R. Vogel and M. E. Oman, A fast, robust algorithm for total variation based reconstruction of noisy, blurred images, IEEE Trans. Image Process., 7 (1998), 813-824. doi: 10.1109/83.679423.  Google Scholar

[20]

D. Watkins, "Fundamentals of Matrix Computations," Wiley, Hoboken, NJ, 2010.  Google Scholar

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