V. Manya, . Afonso, M. José, . Bioucas-dias, A. Mário et al., Fast image recovery using variable splitting and constrained optimization, IEEE Transactions on Image Processing, vol.19, issue.9, pp.2345-2356, 2010.

V. Manya, . Afonso, M. José, . Bioucas-dias, A. Mário et al., An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems, IEEE Transactions on Image Processing, vol.20, issue.3, pp.681-695, 2011.

M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, Image coding using wavelet transform, IEEE Transactions on Image Processing, vol.1, issue.2, pp.205-220, 1992.
DOI : 10.1109/83.136597

URL : https://hal.archives-ouvertes.fr/hal-01322224

J. Ambrose, D. Gooding, and . Uttley, EMI scan in the management of head injuries. The Lancet, pp.847-848, 1976.

H. Anders, . Andersen, C. Avinash, and . Kak, Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm, Ultrasonic Imaging, vol.21, issue.1, pp.81-94, 1984.
DOI : 10.1007/BF01436376

H. Ayasso and A. Mohammad-djafari, Joint NDT Image Restoration and Segmentation Using Gauss???Markov???Potts Prior Models and Variational Bayesian Computation, IEEE Transactions on Image Processing, vol.19, issue.9, pp.2265-2277, 2010.
DOI : 10.1109/TIP.2010.2047902

URL : https://hal.archives-ouvertes.fr/hal-00494945

R. Simon and . Arridge, Optical tomography in medical imaging, Inverse Problems, vol.15, issue.2, p.41, 1999.

A. Aldroubi and M. Unser, Wavelets in medicine and biology, 1996.

M. R. , A. , and H. Zaidi, Development and validation of MCNP4C-based Monte Carlo simulator for fan-and cone-beam x-ray CT, Physics in Medicine and Biology, vol.50, issue.20, p.4863, 2005.

G. Richard and . Baraniuk, Compressive sensing [lecture notes]. IEEE signal processing magazine, pp.118-121, 2007.

R. Battiti, First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method, Neural Computation, vol.8, issue.3, pp.141-166, 1992.
DOI : 10.1162/neco.1989.1.4.425

M. Beister, D. Kolditz, and W. A. Kalender, Iterative reconstruction methods in X-ray CT, Physica Medica, vol.28, issue.2, pp.94-108, 2012.
DOI : 10.1016/j.ejmp.2012.01.003

M. Bhatia, W. C. Karl, and A. S. Willsky, A wavelet-based method for multiscale tomographic reconstruction, IEEE Transactions on Medical Imaging, vol.15, issue.1, pp.92-101, 1996.
DOI : 10.1109/42.481444

X. Boespflug, S. Long, and . Occhietti, CAT-scan in marine stratigraphy: a quantitative approach, Marine Geology, vol.122, issue.4, pp.281-301, 1995.
DOI : 10.1016/0025-3227(94)00129-9

N. Bali and A. Mohammad-djafari, Bayesian Approach With Hidden Markov Modeling and Mean Field Approximation for Hyperspectral Data Analysis, IEEE Transactions on Image Processing, vol.17, issue.2, pp.217-225, 2008.
DOI : 10.1109/TIP.2007.914227

URL : https://hal.archives-ouvertes.fr/hal-00449832

L. Bottou, Large-scale machine learning with stochastic gradient descent, Proceedings of COMPSTAT'2010, pp.177-186, 2010.
DOI : 10.1201/b11429-4

N. Bochkina and T. Sapatinas, On the posterior median estimators of possibly sparse sequences, Annals of the Institute of Statistical Mathematics, vol.32, issue.2, pp.315-351, 2005.
DOI : 10.1007/BF02507028

K. Batenburg and J. Sijbers, DART: A Practical Reconstruction Algorithm for Discrete Tomography, IEEE Transactions on Image Processing, vol.20, issue.9, pp.2542-2553, 2011.
DOI : 10.1109/TIP.2011.2131661

S. Boyd and L. Vandenberghe, Convex optimization, 2004.

Z. Su-bangliang, P. Yiheng, Y. Lihui, Z. Danya, and . Baofen, The use of simultaneous iterative reconstruction technique for electrical capacitance tomography, Chemical Engineering Journal, vol.77, issue.1-2, pp.37-41, 2000.
DOI : 10.1016/S1385-8947(99)00134-5

A. Corduneanu, M. Christopher, and . Bishop, Variational Bayesian model selection for mixture distributions, In Artificial intelligence and Statistics, pp.27-34, 2001.

L. Candes, D. Demanet, L. Donoho, and . Ying, Fast Discrete Curvelet Transforms, Multiscale Modeling & Simulation, vol.5, issue.3, pp.861-899, 2006.
DOI : 10.1137/05064182X

L. Roger, . Claypoole, M. Geoffrey, W. Davis, . Sweldens et al., Nonlinear wavelet transforms for image coding via lifting, IEEE Transactions on Image Processing, vol.12, issue.12, pp.1449-1459, 2003.

F. Tony, . Chan, H. Gene, P. Golub, and . Mulet, A nonlinear primal-dual method for total variation-based image restoration, SIAM journal on scientific computing, vol.20, issue.6, pp.1964-1977, 1999.

Y. Choi, J. Koo, and N. Lee, Image reconstruction using the wavelet transform for positron emission tomography, IEEE Transactions on Medical Imaging, vol.20, issue.11, pp.1188-1193, 2001.
DOI : 10.1109/42.963822

A. Chambolle and P. Lions, Image recovery via total variation minimization and related problems, Numerische Mathematik, vol.76, issue.2, pp.167-188, 1997.
DOI : 10.1007/s002110050258

S. Coric, M. Leeser, E. Miller, and M. Trepanier, Parallel-beam backprojection, Proceedings of the 2002 ACM/SIGDA tenth international symposium on Field-programmable gate arrays , FPGA '02, pp.217-226, 2002.
DOI : 10.1145/503048.503080

N. Chetih and Z. Messali, Tomographic image reconstruction using filtered back projection (FBP) and algebraic reconstruction technique (ART), 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), pp.1-6, 2015.
DOI : 10.1109/CEIT.2015.7233031

D. Calvetti, F. Morigi, and . Sgallari, Tikhonov regularization and the L-curve for large discrete ill-posed problems, Journal of Computational and Applied Mathematics, vol.123, issue.1-2, pp.423-446, 2000.
DOI : 10.1016/S0377-0427(00)00414-3

H. Alexander, Y. Delaney, and . Bresler, Multiresolution tomographic reconstruction using wavelets, IEEE Transactions on Image Processing, vol.4, issue.6, pp.799-813, 1995.

R. Stanley and . Deans, The Radon transform and some of its applications, Courier Corporation, 2007.

[. Dumitru, N. Gac, L. Wang, and A. Mohammad-djafari, Unsupervised sparsity enforcing iterative algorithms for 3D image reconstruction in X-ray computed tomography, The 2017 International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01568325

[. Dumitru, W. Li, N. Gac, and A. Mohammad-djafari, Performance comparison of Bayesian iterative algorithms for three classes of sparsity enforcing priors with application in computed tomography, 2017 IEEE International Conference on Image Processing, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01568337

L. David and . Donoho, Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006.

L. Dettori and L. Semler, A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography. Computers in biology and medicine, pp.486-498, 2007.

M. Dumitru, A Bayesian approach for periodic components estimation for chronobiological signals, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01318048

N. Minh, M. Do, and . Vetterli, The contourlet transform : an efficient directional multiresolution image representation, IEEE Transactions on Image Processing, vol.14, issue.12, pp.2091-2106, 2005.

[. Dumitru, L. Wang, N. Gac, and A. Mohammad-djafari, Comparaison des performances d'algorithmes itératifs bayésiens basés sur trois classes de modeles a priori parcimonieux appliquésappliqués`appliquésà la reconstruction tomographique, GRETSI, 2017.

[. Dumitru, L. Wang, A. Mohammad-djafari, and N. Gac, Model selection in the sparsity context for inverse problems in Bayesian framework, 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01568318

M. Elad, P. Milanfar, and R. Rubinstein, Analysis versus synthesis in signal priors, Inverse Problems, vol.23, issue.3, p.947, 2007.
DOI : 10.1088/0266-5611/23/3/007

URL : http://www.cs.technion.ac.il/%7Eronrubin/Publications/eusipco_avs.pdf

A. William and . Ericson, A note on the posterior mean of a population mean, Journal of the Royal Statistical Society. Series B (Methodological), pp.332-334, 1969.

A. Jeffrey, . Fessler, D. Scott, and . Booth, Conjugate-gradient preconditioning methods for shift-variant PET image reconstruction, IEEE Transactions on Image Processing, vol.8, issue.5, pp.688-699, 1999.

T. Frese, A. Charles, K. Bouman, and . Sauer, Adaptive wavelet graph model for Bayesian tomographic reconstruction, IEEE Transactions on Image Processing, vol.11, issue.7, pp.756-770, 2002.
DOI : 10.1109/TIP.2002.801586

URL : http://www.ece.purdue.edu/~bouman/publications/pdf/ip17.pdf

L. Feldkamp, J. Davis, and . Kress, Practical cone-beam algorithm, Journal of the Optical Society of America A, vol.1, issue.6, pp.612-619, 1984.
DOI : 10.1364/JOSAA.1.000612

URL : http://www.engineering.uiowa.edu/~mchen/reconstruction/practical feldkamp.pdf

A. Fischer, A special Newton-type optimization method. Optimization, pp.269-284, 1992.
DOI : 10.1080/02331939208843795

W. Charles, . Fox, J. Stephen, and . Roberts, A tutorial on variational Bayesian inference, Artificial intelligence review, vol.38, issue.2, pp.85-95, 2012.

M. Feurer, J. T. Springenberg, and F. Hutter, Initializing Bayesian Hyperparameter Optimization via Meta-learning, 2015.

J. Gawler, . Bull, J. Boulay, and . Marshall, COMPUTER-ASSISTED TOMOGRAPHY (EMI SCANNER) ITS PLACE IN INVESTIGATION OF SUSPECTED INTRACRANIAL TUMOURS, The Lancet, vol.304, issue.7878, pp.419-423, 1974.
DOI : 10.1016/S0140-6736(74)91813-3

R. Gordon, R. Bender, T. Gabor, and . Herman, Algebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography, Journal of Theoretical Biology, vol.29, issue.3, pp.471-1477, 1970.
DOI : 10.1016/0022-5193(70)90109-8

A. Gelman, B. John, . Carlin, S. Hal, . Stern et al., Bayesian data analysis, 2014.

H. Gene, M. Golub, G. Heath, and . Wahba, Generalized cross-validation as a method for choosing a good ridge parameter, Technometrics, vol.21, issue.2, pp.215-223, 1979.

D. Gerónimo, A. López, D. Ponsa, D. Angel, and . Sappa, Haar Wavelets and Edge Orientation Histograms for On???Board Pedestrian Detection, Iberian Conference on Pattern Recognition and Image Analysis, pp.418-425, 2007.
DOI : 10.1007/978-3-540-72847-4_54

T. Goldstein and S. Osher, The Split Bregman Method for L1-Regularized Problems, SIAM Journal on Imaging Sciences, vol.2, issue.2, pp.323-343, 2009.
DOI : 10.1137/080725891

R. Gordon, A tutorial on art (algebraic reconstruction techniques), IEEE Transactions on Nuclear Science, vol.21, issue.3, pp.78-93, 1974.
DOI : 10.1109/TNS.1974.6499238

G. Gindi, A. Rangarajan, . Lee, and . Hong, Bayesian reconstruction for emission tomography via deterministic annealing, Biennial International Conference on Information Processing in Medical Imaging, pp.322-338, 1993.
DOI : 10.1007/BFb0013797

URL : http://www.cis.ufl.edu/~anand/ps/ipmi93.ps.gz

R. Hanke, T. Fuchs, and N. Uhlmann, X-ray based methods for non-destructive testing and material characterization. Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment, pp.14-18, 2008.
DOI : 10.1016/j.nima.2008.03.016

H. Malcolm, H. Richard, and S. Larkin, Accelerated image reconstruction using ordered subsets of projection data, IEEE Transactions on Medical Imaging, vol.13, issue.4, pp.601-609, 1994.

F. Forrest, . Hopkins, L. Ira, . Morgan, D. Hunter et al., Industrial tomography applications, IEEE Transactions on Nuclear Science, vol.28, issue.2, pp.1717-1720, 1981.

C. Per, D. Hansen, O. Prost, and . Leary, The use of the L-curve in the regularization of discrete ill-posed problems, SIAM Journal on Scientific Computing, vol.14, issue.6, pp.1487-1503, 1993.

T. Carl and . Kelley, Iterative methods for optimization, SIAM, 1999.

G. Nick and . Kingsbury, The dual-tree complex wavelet transform : a new technique for shift invariance and directional filters, IEEE Digital Signal Processing Workshop, pp.120-131, 1998.

L. Borisovi? and K. , Heavy tailed distributions, Matfyzpress, 2003.

S. Klein, P. Josien, M. Pluim, M. A. Staring, and . Viergever, Adaptive Stochastic Gradient Descent Optimisation for Image Registration, International Journal of Computer Vision, vol.21, issue.11, pp.227-239, 2009.
DOI : 10.1016/S1361-8415(01)80026-8

URL : https://pure.tue.nl/ws/files/3967424/671219743810168.pdf

A. Ville-kolehmainen, S. Vanne, S. Siltanen, . Jarvenpaa, P. Jari et al., Parallelized Bayesian inversion for three-dimensional dental X-ray imaging, IEEE Transactions on Medical Imaging, vol.25, issue.2, pp.218-228, 2006.
DOI : 10.1109/TMI.2005.862662

I. Loris, G. Nolet, I. Daubechies, and F. Dahlen, -norm regularization of wavelet coefficients, Geophysical Journal International, vol.158, issue.1, pp.359-370, 2007.
DOI : 10.1007/978-94-009-2857-2_8

R. Ledley, . Wilson, L. Golab, and . Rotolo, The ACTA-Scanner: The whole body computerized transaxial tomograph, Computers in biology and medicine, pp.145-7153, 1974.
DOI : 10.1016/0010-4825(74)90016-X

A. Mohammad-djafari, Joint estimation of parameters and hyperparameters in a Bayesian approach of solving inverse problems, Proceedings of 3rd IEEE International Conference on Image Processing, pp.473-476, 1996.
DOI : 10.1109/ICIP.1996.560890

D. Maclaurin, D. Duvenaud, and R. Adams, Gradient-based hyperparameter optimization through reversible learning, International Conference on Machine Learning, pp.2113-2122, 2015.

K. Todd and . Moon, The expectation-maximization algorithm, IEEE Signal Processing Magazine, vol.13, issue.6, pp.47-60, 1996.

C. Masih-nilchian, S. Vonesch, P. Lefkimmiatis, M. Modregger, M. Stampanoni et al., Constrained regularized reconstruction of X-ray-DPCI tomograms with weighted-norm, Optics Express, vol.21, issue.26, pp.32340-32348, 2013.
DOI : 10.1364/OE.21.032340

D. Needell, R. Ward, and N. Srebro, Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm, Advances in Neural Information Processing Systems, pp.1017-1025, 2014.
DOI : 10.1137/120889897

. Ommaya, . Murray, . Ambrose, G. Richardson, and . Hounsfield, Computerized Axial Tomography: Estimation of Spatial and Density Resolution Capability, The British Journal of Radiology, vol.110, issue.583, pp.604-611, 1976.
DOI : 10.3109/00016923209135135

R. Paxton and J. Ambrose, The EMI scanner. A brief review of the first 650 patients. The British journal of radiology, pp.530-565, 1974.

M. Petrou and C. Petrou, Image processing : the fundamentals, 2010.
DOI : 10.1002/9781119994398

J. Portilla, V. Strela, J. Martin, . Wainwright, P. Eero et al., Image denoising using scale mixtures of gaussians in the wavelet domain, IEEE Transactions on Image Processing, vol.12, issue.11, pp.1338-1351, 2003.
DOI : 10.1109/TIP.2003.818640

A. Pts-+-15-]-françoise-peyrin, B. Toma, L. Sixou, A. Denis, J. Burghardt et al., Semi-blind joint super-resolution/segmentation of 3D trabecular bone images by a TV box approach, Signal Processing Conference (EUSIPCO), 2015 23rd European, pp.2811-2815, 2015.

J. Qi, M. Richard, and . Leahy, Resolution and noise properties of MAP reconstruction for fully 3-D PET, IEEE Transactions on Medical Imaging, vol.19, issue.5, pp.493-506, 2000.

J. Radon, Uber die bestimmug von funktionen durch ihre integralwerte laengs geweisser mannigfaltigkeiten, Berichte Saechsishe Acad. Wissenschaft. Math. Phys., Klass, vol.69, p.262, 1917.
DOI : 10.1090/psapm/027/692055

S. Ramani, A. Jeffrey, and . Fessler, Statistical X-ray CT reconstruction using a splitting-based iterative algorithm with orthonormal wavelets, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.1008-1011, 2012.
DOI : 10.1109/ISBI.2012.6235728

Z. Sathish-ramani, J. Liu, J. Rosen, . Nielsen, A. Jeffrey et al., Regularization Parameter Selection for Nonlinear Iterative Image Restoration and MRI Reconstruction Using GCV and SURE-Based Methods, IEEE Transactions on Image Processing, vol.21, issue.8, pp.3659-3672, 2012.
DOI : 10.1109/TIP.2012.2195015

S. Rit, D. Sarrut, and L. Desbat, Comparison of Analytic and Algebraic Methods for Motion-Compensated Cone-Beam CT Reconstruction of the Thorax, IEEE Transactions on Medical Imaging, vol.28, issue.10, pp.1513-1525, 2009.
DOI : 10.1109/TMI.2008.2008962

URL : https://hal.archives-ouvertes.fr/hal-00443440

S. Rantala, S. Vanska, M. Jarvenpaa, M. Kalke, S. Lassas et al., Wavelet-based reconstruction for limited-angle X-ray tomography, IEEE Transactions on Medical Imaging, vol.25, issue.2, pp.210-217, 2006.
DOI : 10.1109/TMI.2005.862206

A. Richard, . Redner, F. Homer, and . Walker, Mixture densities, maximum likelihood and the EM algorithm, SIAM Review, vol.26, issue.2, pp.195-239, 1984.

DOI : 10.1016/B978-0-08-092534-9.50007-2

K. Sauer and C. Bouman, Bayesian estimation of transmission tomograms using segmentation based optimization, IEEE Transactions on Nuclear Science, vol.39, issue.4, pp.1144-1152, 1992.
DOI : 10.1109/23.159774

K. Sauer and C. Bouman, A local update strategy for iterative reconstruction from projections, IEEE Transactions on Signal Processing, vol.41, issue.2, pp.534-548, 1993.
DOI : 10.1109/78.193196

URL : http://www.ece.purdue.edu/~bouman/publications/pdf/sp2.pdf

W. Ivan, . Selesnick, G. Richard, . Baraniuk, C. Nick et al., The dual-tree complex wavelet transform, IEEE Signal Processing Magazine, vol.22, issue.6, pp.123-151, 2005.

S. Suhail, . Saquib, A. Charles, K. Bouman, and . Sauer, ML parameter estimation for Markov random fields with applications to Bayesian tomography, IEEE Transactions on Image Processing, vol.7, issue.7, pp.1029-1044, 1998.

H. Jacques, S. Gregory, and F. Cooper, Initialization for the method of conditioning in Bayesian belief networks, Artificial Intelligence, vol.50, issue.1, pp.83-94, 1991.

P. Sukovic, H. Neal, and . Clinthorne, Penalized weighted least-squares image reconstruction for dual energy X-ray transmission tomography, IEEE Transactions on Medical Imaging, vol.19, issue.11, pp.1075-1081, 2000.
DOI : 10.1109/42.896783

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1619874/pdf

A. John and . Scales, Tomographic inversion via the conjugate gradient method, Geophysics, vol.52, issue.2, pp.179-185, 1987.

J. Starck, J. Emmanuel, . Candès, L. David, and . Donoho, The curvelet transform for image denoising, IEEE Transactions on Image Processing, vol.11, issue.6, pp.670-684, 2002.
DOI : 10.1109/TIP.2002.1014998

S. Radomir, . Stankovi´cstankovi´c, J. Bogdan, and . Falkowski, The Haar wavelet transform : its status and achievements, Computers & Electrical Engineering, vol.29, issue.1, pp.25-44, 2003.

D. Sridhar and I. Krishna, Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network, 2013 International Conference on Signal Processing , Image Processing & Pattern Recognition, pp.92-96, 2013.
DOI : 10.1109/ICSIPR.2013.6497966

Y. Emil, X. Sidky, and . Pan, Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization, Physics in Medicine and Biology, vol.53, issue.17, p.4777, 2008.

. Da-tregouet, . Escolano, . Tiret, J. Mallet, and . Golmard, A new algorithm for haplotype-based association analysis: the Stochastic-EM algorithm, Annals of Human Genetics, vol.85, issue.2, pp.165-177, 2004.
DOI : 10.1159/000048602

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

J. Trampert and J. Leveque, Simultaneous iterative reconstruction technique: Physical interpretation based on the generalized least squares solution, Journal of Geophysical Research, vol.10, issue.B8, pp.95553-95562, 1990.
DOI : 10.1029/RG010i001p00251

G. Dimitris, . Tzikas, C. Aristidis, . Likas, P. Nikolaos et al., The variational approximation for Bayesian inference, IEEE Signal Processing Magazine, vol.25, issue.6, pp.131-146, 2008.

V. Tresp, Mixtures of Gaussian processes, Advances in neural information processing systems, pp.654-660, 2001.

J. Setoain, M. Prieto, L. Piñuel, and F. Tirado, Parallel implementation of the 2d discrete wavelet transform on graphics processing units : Filter bank versus lifting, IEEE Transactions on Parallel and Distributed Systems, vol.19, issue.3, pp.299-310, 2008.

X. Tai and C. Wu, Augmented Lagrangian method, dual methods and split Bregman iteration for ROF model. Scale space and variational methods in computer vision, pp.502-513, 2009.
DOI : 10.1007/978-3-642-02256-2_42

W. J. Wim-van-aarle, J. Palenstijn, E. Cant, F. Janssens, A. Bleichrodt et al., Fast and flexible X-ray tomography using the ASTRA toolbox, Optics Express, vol.24, issue.22, pp.2425129-25147, 2016.
DOI : 10.1364/OE.24.025129

M. Vorontsov, J. Carhart, and . Ricklin, Adaptive phase-distortion correction based on parallel gradient-descent optimization, Optics Letters, vol.22, issue.12, pp.907-909, 1997.
DOI : 10.1364/OL.22.000907

Y. Vardi, L. Shepp, and L. Kaufman, A Statistical Model for Positron Emission Tomography, Journal of the American Statistical Association, vol.31, issue.4, pp.8-20, 1985.
DOI : 10.1214/aos/1176345692

B. Wahlberg, S. Boyd, M. Annergren, and Y. Wang, An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems*, IFAC Proceedings Volumes, pp.83-88, 2012.
DOI : 10.3182/20120711-3-BE-2027.00310

L. Wang, N. Gac, and A. Mohammad-djafari, Bayesian 3D X-ray computed tomography image reconstruction with a scaled Gaussian mixture prior model, AIP Conference Proceedings, pp.556-563, 2015.
DOI : 10.1063/1.4906022

URL : https://hal.archives-ouvertes.fr/hal-01338706

L. Wang, N. Gac, and A. Mohammad-djafari, Reconstruction 3D en to- mographiè a rayons X ` a l'aide d'un modèle a priori hiérarchique utilisant la transformation de Haar, 2017.

D. Wolf, H. Lubk, and . Lichte, Weighted simultaneous iterative reconstruction technique for single-axis tomography, Ultramicroscopy, vol.136, pp.15-25, 2014.
DOI : 10.1016/j.ultramic.2013.07.016

[. Wang, A. Mohammad-djafari, and N. Gac, Bayesian method with sparsity enforcing prior of dual-tree complex wavelet transform coefficients for X-ray CT image reconstruction, 2017 25th European Signal Processing Conference (EUSIPCO), 2017.
DOI : 10.23919/EUSIPCO.2017.8081253

URL : https://hal.archives-ouvertes.fr/hal-01567875

L. Wang, A. Mohammad-djafari, and N. Gac, Bayesian X-ray computed tomography using a three-level hierarchical prior model, AIP Conference Proceedings, p.60003, 2017.
DOI : 10.1137/0914086

URL : https://hal.archives-ouvertes.fr/hal-01403790

L. Wang, A. Mohammad-djafari, and N. Gac, X-ray Computed Tomography simultaneous image reconstruction and contour detection using a hierarchical Markovian model, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017.
DOI : 10.1109/ICASSP.2017.7953322

URL : https://hal.archives-ouvertes.fr/hal-01490508

[. Wang, A. Mohammad-djafari, and N. Gac, X-ray Computed Tomography using a Sparsity Enforcing Prior Model Based on Haar Transformation in a Bayesian Framework, Fundamenta Informaticae, vol.155, issue.4, pp.449-480, 2017.
DOI : 10.3233/FI-2017-1594

URL : https://hal.archives-ouvertes.fr/hal-01490523

[. Wang, A. Mohammad-djafari, N. Gac, and M. Dumitru, Computed tomography reconstruction based on a hierarchical model and variational Bayesian method, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.883-887, 2016.
DOI : 10.1109/ICASSP.2016.7471802

URL : https://hal.archives-ouvertes.fr/hal-01403784

[. Wang, A. Mohammad-djafari, N. Gac, and M. Dumitru, 3D Xray Computed Tomography reconstruction using sparsity enforcing hierarchical model based on Haar transformation, The 2017 International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017.
DOI : 10.3233/fi-2017-1594

URL : https://hal.archives-ouvertes.fr/hal-01490554

G. Wang, L. Schultz, and J. Qi, Statistical Image Reconstruction for Muon Tomography Using a Gaussian Scale Mixture Model, IEEE Transactions on Nuclear Science, vol.56, issue.4, pp.2480-2486, 2009.
DOI : 10.1109/TNS.2009.2023518

Z. Wu, Frequency and noise dependence of the image reconstruction of ground surfaces using the conjugate gradient based algorithm, IEE Proceedings-Radar, Sonar and Navigation, pp.211-218, 2001.
DOI : 10.1049/ip-rsn:20010322

L. Scott, . Wellington, J. Harold, and . Vinegar, X-ray computerized tomography, Journal of Petroleum Technology, vol.87, issue.3908, pp.885-898, 1987.

W. David, . Winters, D. Barry, . Van-veen, C. Susan et al., A sparsity regularization approach to the electromagnetic inverse scattering problem, IEEE Transactions on Antennas and Propagation, vol.58, issue.1, pp.145-154, 2010.

Y. Wang, J. Yang, W. Yin, and Y. Zhang, A New Alternating Minimization Algorithm for Total Variation Image Reconstruction, SIAM Journal on Imaging Sciences, vol.1, issue.3, pp.248-272, 2008.
DOI : 10.1137/080724265

URL : http://epubs.siam.org/doi/pdf/10.1137/080724265

H. Xu, X. Yu, L. Mou, J. Zhang, G. Hsieh et al., Low-dose X-ray CT reconstruction via dictionary learning, IEEE Transactions on Medical Imaging, issue.9, pp.311682-1697, 2012.

T. Yuasa, M. Akiba, T. Takeda, M. Kazama, A. Hoshino et al., Reconstruction method for fluorescent X-ray computed tomography by least-squares method using singular value decomposition, IEEE Transactions on Nuclear Science, vol.44, issue.1, pp.54-62, 1997.
DOI : 10.1109/23.554824

Y. Zheng, A. Fraysse, and T. Rodet, Efficient Variational Bayesian Approximation Method Based on Subspace Optimization, IEEE Transactions on Image Processing, vol.24, issue.2, pp.681-693, 2015.
DOI : 10.1109/TIP.2014.2383321

URL : https://hal.archives-ouvertes.fr/hal-00990003

H. Hao-zhang, Z. Han, Y. Liang, Y. Hu, W. Liu et al., Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images, IEEE Transactions on Medical Imaging, vol.35, issue.3, pp.860-870, 2016.
DOI : 10.1109/TMI.2015.2498148

Z. Zhou, J. Leahy, and . Qi, Approximate maximum likelihood hyperparameter estimation for Gibbs priors, Proceedings., International Conference on Image Processing, pp.844-861, 1997.
DOI : 10.1109/ICIP.1995.537470