An efficient computational method for total variation-penalized Poisson likelihood estimation
Pages: 167 - 185,
Volume 2,
Issue 2,
May 2008
doi:10.3934/ipi.2008.2.167 Abstract
References
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Johnathan M. Bardsley - Department of Mathematical Sciences, University of Montana Missoula, Montana 59812, United States (email)
| 1 |
D. P. Bertsekas, On the Goldstein-Levitin-Poljak gradient projection method, IEEE Transactions on Automatic Control, 21 (1976), 174-184. |
|
| 2 |
Johnathan M. Bardsley and James G. Nagy, Covariance-preconditioned iterative methods for nonnegatively constrained astronomical imaging, SIAM Journal on Matrix Analysis and Applications, 27 (2006), 1184-1197. |
|
| 3 |
J. M. Bardsley and C. R. Vogel, A nonnnegatively constrained convex programming method for image reconstruction, SIAM Journal on Scientific Computing, 25 (2004), 1326-1343 (electronic). |
|
| 4 |
Johnathan M. Bardsley and Aaron Luttman, Total variation-penalized Poisson likelihood estimation for ill-posed problems, accepted, Advances in Computational Mathematics, Special Issue on Mathematical Imaging, University of Montana Technical Report #8, 2006. |
|
| 5 |
P. H. Calamai and J. J. Moré, Projected gradient methods for linearly constrained problems, Mathematical Programming, 39 (1987), 93-116. |
|
| 6 |
D. Calvetti, G. Landi, L. Reichel and S. Sgallari, Non-negativity and iterative methods for ill-posed problems, Inverse Problems, 20 (2004), 1747-1758. |
|
| 7 |
Daniela Calvetti and Erkki Somersalo, A Gaussian hypermodel for recovering blocky objects, Inverse Problems, 23 (2007), 733-754. |
|
| 8 |
Torbjørn Eltoft and Taesu Kim, On the multivariate Laplace distribution, IEEE Signal Processing Letters, 13 (2006), 300-303. |
|
| 9 |
J. W. Goodman, "Introduction to Fourier Optics," 2nd Edition, McGraw-Hill, 1996. |
|
| 10 |
M. Green, Statistics of images, the TV algorithm of Rudin-Osher-Fatemi for image denoising, and an improved denoising algorithm, CAM Report 02-55, UCLA, October 2002. |
|
| 11 |
Jinggang Huang and David Mumford, Statistics of natural images and models, in "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition," 1999, 541-547. |
|
| 12 |
Jari Kaipio and Erkki Somersalo, "Satistical and Computational Inverse Problems," Applied Mathematical Sciences, 160, Springer-Verlag, New York, 2005. |
|
| 13 |
C. T. Kelley, "Iterative Methods for Optimization," Frontiers in Applied Mathematics, 18, SIAM, Philadelphia, 1999. |
|
| 14 |
J. J. Moré and G. Toraldo, On the solution of large quadratic programming problems with bound constraints, SIAM Journal on Optimization, 1 (1991), 93-113. |
|
| 15 |
J. Nagy and Z. Strakoš, Enforcing nonnegativity in image reconstruction algorithms, Mathematical Modeling, Estimation, and Imaging, David C. Wilson, et.al., Eds., 4121 (2000), 182-190. |
|
| 16 |
J. Nocedal and S. Wright, "Numerical Optimization," Series in Operations Research. Springer-Verlag, New York, 1999. |
|
| 17 |
R. T. Rockafellar, "Convex Analysis," Princeton University Press, 1970. |
|
| 18 |
L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D, 60 (1992), 259-268. |
|
| 19 |
D. L. Snyder, A. M. Hammoud and R. L. White, Image recovery from data acquired with a charge-coupled-device camera, Journal of the Optical Society of America A, 10 (1993), 1014-1023. |
|
| 20 |
C. R. Vogel, Computational methods for inverse problems, With a foreword by H. T. Banks. Frontiers in Applied Mathematics, 23, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2002. |
|
| 21 |
C. R. Vogel and M. E. Oman, Fast, robust total variation-based reconstruction of noisy, blurred images, IEEE Transactions on Image Processing, 7 (1998), 813-824. |
|
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