Blind Deconvolution of Sparse Pulse Sequences Under a Minimum Distance Constraint: A Partially Collapsed Gibbs Sampler Method, IEEE Transactions on Signal Processing, vol.60, issue.6, pp.2727-2743, 2012. ,
DOI : 10.1109/TSP.2012.2190066
Fast texture synthesis using tree-structured vector quantization, Proceedings of the 27th annual conference on Computer graphics and interactive techniques , SIGGRAPH '00, pp.479-488, 2000. ,
DOI : 10.1145/344779.345009
URL : http://www.cs.stevens.edu/~quynh/courses/cs638-papers/wei_textsyn.pdf
Texture feature based on local Fourier transform, Proc. Int. Conf. Image Process, pp.610-613, 2001. ,
Synthesizing and Mixing Stationary Gaussian Texture Models, SIAM Journal on Imaging Sciences, vol.7, issue.1, pp.476-508, 2014. ,
DOI : 10.1137/130918010
URL : https://hal.archives-ouvertes.fr/hal-00988761
Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models, IEEE Transactions on Multimedia, vol.4, issue.4, pp.517-527, 2002. ,
DOI : 10.1109/TMM.2002.802019
URL : https://infoscience.epfl.ch/record/33833/files/DoV02d.pdf
A parametric texture model based on joint statistics of complex wavelet coefficients, International Journal of Computer Vision, vol.40, issue.1, pp.49-71, 2000. ,
DOI : 10.1023/A:1026553619983
Classification of textures using Gaussian Markov random fields, ASSP-33, pp.959-963, 1985. ,
DOI : 10.1109/TASSP.1985.1164641
Texture synthesis using 2-D noncausal autoregressive models, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.33, issue.1, pp.194-203, 1985. ,
DOI : 10.1109/TASSP.1985.1164507
Filters, random fields and maximum entropy (frame)?Towards a unified theory for texture modeling, Int. J. Comput. Vis, vol.27, issue.2, pp.1-20, 1998. ,
Statistical Modeling of Photographic Images, Handbook of Video and Image Processing, 2005. ,
DOI : 10.1016/B978-012119792-6/50089-9
A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974. ,
DOI : 10.1109/TAC.1974.1100705
Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.93, issue.4, pp.583-639, 2002. ,
DOI : 10.1002/1097-0258(20000915/30)19:17/18<2265::AID-SIM568>3.0.CO;2-6
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Variational algorithms for approximate bayesian inference, 2003. ,
Hypothesis testing and model selection via posterior simulation, 1995. ,
Optimal Proposal Distributions and Adaptive MCMC, pp.93-112, 2011. ,
DOI : 10.1201/b10905-5
URL : http://probability.ca/jeff/ftpdir/galinart.pdf
Metropolis???Hastings algorithms with adaptive proposals, Statistics and Computing, vol.18, issue.3, pp.421-433, 2008. ,
DOI : 10.1007/978-1-4615-3598-0
Langevin diffusions and Metropolis-Hastings algorithms, Methodology and Computing in Applied Probability, vol.4, issue.4, pp.337-358, 2003. ,
DOI : 10.1023/A:1023562417138
Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.13, issue.10, pp.123-214, 2011. ,
DOI : 10.1162/08997660460734047
URL : http://www.stat.columbia.edu/%7Ecook/movabletype/mlm/RMHMC_MG_BC_SC_REV_08_04_10.pdf
MCMC Using Hamiltonian Dynamics, pp.93-112, 2011. ,
DOI : 10.1201/b10905-6
URL : http://www.mcmchandbook.net/HandbookChapter5.pdf
Natural Gradient Works Efficiently in Learning, Neural Computation, vol.37, issue.2, pp.251-276, 1998. ,
DOI : 10.1103/PhysRevLett.76.2188
Hessian-based Markov Chain Monte-Carlo algorithms, " presented at the 1st Cape Cod Workshop Monte Carlo Methods, 2002. ,
Scaled stochastic Newton algorithm for Markov chain Monte Carlo simulations, 2012. ,
A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion, SIAM Journal on Scientific Computing, vol.34, issue.3, pp.1460-1487, 2012. ,
DOI : 10.1137/110845598
Langevin and hessian with fisher approximation stochastic sampling for parameter estimation of structured covariance, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.3964-3967, 2011. ,
DOI : 10.1109/ICASSP.2011.5947220
URL : https://hal.archives-ouvertes.fr/hal-00668308
The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, ser. Springer Texts in Statist, 2007. ,
DOI : 10.1007/978-1-4757-4314-2
Bayesian texture model selection using harmonic mean, 2012. ,
DOI : 10.1109/icip.2012.6467414
Nonlinear image recovery with half-quadratic regularization, IEEE Transactions on Image Processing, vol.4, issue.7, pp.932-946, 1995. ,
DOI : 10.1109/83.392335
URL : http://www.math.umass.edu/~geman/Papers/nonlinear.ps.gz
Constrained restoration and the recovery of discontinuities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.3, pp.367-383, 1992. ,
DOI : 10.1109/34.120331
Unsupervised Bayesian Convex Deconvolution Based on a Field With an Explicit Partition Function, IEEE Transactions on Image Processing, vol.17, issue.1, pp.16-26, 2008. ,
DOI : 10.1109/TIP.2007.911819
URL : https://hal.archives-ouvertes.fr/hal-00879198
Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors, IEEE Transactions on Image Processing, vol.15, issue.4, pp.937-951, 2006. ,
DOI : 10.1109/TIP.2005.863972
Image deconvolution using a Gaussian scale mixtures model to approximate the wavelet sparseness constraint, Proc. IEEE ICASSP, pp.681-684, 2009. ,
Random Cascades on Wavelet Trees and Their Use in Analyzing and Modeling Natural Images, Applied and Computational Harmonic Analysis, vol.11, issue.1, pp.89-123, 2001. ,
DOI : 10.1006/acha.2000.0350
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
Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures, IEEE Transactions on Image Processing, vol.17, issue.11, pp.2089-2101, 2008. ,
DOI : 10.1109/TIP.2008.2004796
URL : http://www.cns.nyu.edu/pub/lcv/hammond08-reprint.pdf
Gaussian Markov Random Fields: Theory and Applications, Monographs on Statist. Appl. Probabil, vol.104, 2005. ,
DOI : 10.1201/9780203492024
Random Fields on a Network: Modelling, Statistics, and Applications, Probability and Its Applications, 1995. ,
Signals and Systems, 1983. ,
Principles of Signals and Systems: Deterministic Signals, 1988. ,
Nested sampling for general Bayesian computation, Bayesian Analysis, vol.1, issue.4, 2006. ,
DOI : 10.1214/06-BA127
URL : http://doi.org/10.1214/06-ba127
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995. ,
DOI : 10.1093/biomet/82.4.711
Bayesian model choice: Asymptotics and exact calculations, J. Roy. Statist. Soc. B, vol.56, issue.3, pp.501-514, 1994. ,
DOI : 10.21236/ADA269067
URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a269067.pdf
Approximate Bayesian inference with the weighted likelihood bootstrap, J. Roy. Stat. Soc. B, vol.56, issue.1, pp.3-48, 1994. ,
On the convergence of moments of geometric and harmonic means, Statistica Neerlandica, vol.53, issue.1, pp.96-110, 1999. ,
DOI : 10.1111/1467-9574.00100
Estimating the integrated likelihood via posterior simulation using the harmonic mean identity, Bayesian Statist, pp.1-45, 2007. ,
Bayes Factors, Journal of the American Statistical Association, vol.2, issue.430, pp.773-795, 1995. ,
DOI : 10.1214/ss/1177013241
Monte Carlo Statistical Methods, 2004. ,
Markov Chains for Exploring Posterior Distributions, The Annals of Statistics, vol.22, issue.4, pp.1701-1762, 1994. ,
DOI : 10.1214/aos/1176325750
URL : http://doi.org/10.1214/aos/1176325750
Monte-Carlo Statistical Methods, ser. Springer Texts in Statistics, 2004. ,
Bayesian estimation of regularization and point spread function parameters for Wiener???Hunt deconvolution, Journal of the Optical Society of America A, vol.27, issue.7, pp.1593-1607, 2010. ,
DOI : 10.1364/JOSAA.27.001593
URL : https://hal.archives-ouvertes.fr/hal-00674508
Bayesian Texture and Instrument Parameter Estimation From Blurred and Noisy Images Using MCMC, IEEE Signal Processing Letters, vol.21, issue.6, pp.707-711, 2014. ,
DOI : 10.1109/LSP.2014.2313274
URL : https://hal.archives-ouvertes.fr/hal-00975094
Blind Deconvolution of Sparse Pulse Sequences Under a Minimum Distance Constraint: A Partially Collapsed Gibbs Sampler Method, IEEE Transactions on Signal Processing, vol.60, issue.6, pp.2727-2743, 2012. ,
DOI : 10.1109/TSP.2012.2190066
Fast texture synthesis using tree-structured vector quantization, Proceedings of the 27th annual conference on Computer graphics and interactive techniques , SIGGRAPH '00, pp.479-488, 2000. ,
DOI : 10.1145/344779.345009
URL : http://www.cs.stevens.edu/~quynh/courses/cs638-papers/wei_textsyn.pdf
Texture feature based on local Fourier transform, Proc. Int. Conf. Image Process, pp.610-613, 2001. ,
Synthesizing and Mixing Stationary Gaussian Texture Models, SIAM Journal on Imaging Sciences, vol.7, issue.1, pp.476-508, 2014. ,
DOI : 10.1137/130918010
URL : https://hal.archives-ouvertes.fr/hal-00988761
Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models, IEEE Transactions on Multimedia, vol.4, issue.4, pp.517-527, 2002. ,
DOI : 10.1109/TMM.2002.802019
URL : https://infoscience.epfl.ch/record/33833/files/DoV02d.pdf
A parametric texture model based on joint statistics of complex wavelet coefficients, International Journal of Computer Vision, vol.40, issue.1, pp.49-71, 2000. ,
DOI : 10.1023/A:1026553619983
Classification of textures using Gaussian Markov random fields, ASSP-33, pp.959-963, 1985. ,
DOI : 10.1109/TASSP.1985.1164641
Texture synthesis using 2-D noncausal autoregressive models, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.33, issue.1, pp.194-203, 1985. ,
DOI : 10.1109/TASSP.1985.1164507
Filters, random fields and maximum entropy (frame)?Towards a unified theory for texture modeling, Int. J. Comput. Vis, vol.27, issue.2, pp.1-20, 1998. ,
Statistical Modeling of Photographic Images, Handbook of Video and Image Processing, 2005. ,
DOI : 10.1016/B978-012119792-6/50089-9
A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974. ,
DOI : 10.1109/TAC.1974.1100705
Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.93, issue.4, pp.583-639, 2002. ,
DOI : 10.1002/1097-0258(20000915/30)19:17/18<2265::AID-SIM568>3.0.CO;2-6
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Variational algorithms for approximate bayesian inference, 2003. ,
Hypothesis testing and model selection via posterior simulation, 1995. ,
Optimal Proposal Distributions and Adaptive MCMC, pp.93-112, 2011. ,
DOI : 10.1201/b10905-5
URL : http://probability.ca/jeff/ftpdir/galinart.pdf
Metropolis???Hastings algorithms with adaptive proposals, Statistics and Computing, vol.18, issue.3, pp.421-433, 2008. ,
DOI : 10.1007/978-1-4615-3598-0
Langevin diffusions and Metropolis-Hastings algorithms, Methodology and Computing in Applied Probability, vol.4, issue.4, pp.337-358, 2003. ,
DOI : 10.1023/A:1023562417138
Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.13, issue.10, pp.123-214, 2011. ,
DOI : 10.1162/08997660460734047
URL : http://www.stat.columbia.edu/%7Ecook/movabletype/mlm/RMHMC_MG_BC_SC_REV_08_04_10.pdf
MCMC Using Hamiltonian Dynamics, pp.93-112, 2011. ,
DOI : 10.1201/b10905-6
URL : http://www.mcmchandbook.net/HandbookChapter5.pdf
Natural Gradient Works Efficiently in Learning, Neural Computation, vol.37, issue.2, pp.251-276, 1998. ,
DOI : 10.1103/PhysRevLett.76.2188
Hessian-based Markov Chain Monte-Carlo algorithms, " presented at the 1st Cape Cod Workshop Monte Carlo Methods, 2002. ,
Scaled stochastic Newton algorithm for Markov chain Monte Carlo simulations, 2012. ,
A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion, SIAM Journal on Scientific Computing, vol.34, issue.3, pp.1460-1487, 2012. ,
DOI : 10.1137/110845598
Langevin and hessian with fisher approximation stochastic sampling for parameter estimation of structured covariance, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.3964-3967, 2011. ,
DOI : 10.1109/ICASSP.2011.5947220
URL : https://hal.archives-ouvertes.fr/hal-00668308
The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, ser. Springer Texts in Statist, 2007. ,
DOI : 10.1007/978-1-4757-4314-2
Bayesian texture model selection using harmonic mean, 2012. ,
DOI : 10.1109/icip.2012.6467414
Nonlinear image recovery with half-quadratic regularization, IEEE Transactions on Image Processing, vol.4, issue.7, pp.932-946, 1995. ,
DOI : 10.1109/83.392335
URL : http://www.math.umass.edu/~geman/Papers/nonlinear.ps.gz
Constrained restoration and the recovery of discontinuities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.3, pp.367-383, 1992. ,
DOI : 10.1109/34.120331
Unsupervised Bayesian Convex Deconvolution Based on a Field With an Explicit Partition Function, IEEE Transactions on Image Processing, vol.17, issue.1, pp.16-26, 2008. ,
DOI : 10.1109/TIP.2007.911819
URL : https://hal.archives-ouvertes.fr/hal-00879198
Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors, IEEE Transactions on Image Processing, vol.15, issue.4, pp.937-951, 2006. ,
DOI : 10.1109/TIP.2005.863972
Image deconvolution using a Gaussian scale mixtures model to approximate the wavelet sparseness constraint, Proc. IEEE ICASSP, pp.681-684, 2009. ,
Random Cascades on Wavelet Trees and Their Use in Analyzing and Modeling Natural Images, Applied and Computational Harmonic Analysis, vol.11, issue.1, pp.89-123, 2001. ,
DOI : 10.1006/acha.2000.0350
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
Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures, IEEE Transactions on Image Processing, vol.17, issue.11, pp.2089-2101, 2008. ,
DOI : 10.1109/TIP.2008.2004796
URL : http://www.cns.nyu.edu/pub/lcv/hammond08-reprint.pdf
Gaussian Markov Random Fields: Theory and Applications, Monographs on Statist. Appl. Probabil, vol.104, 2005. ,
DOI : 10.1201/9780203492024
Random Fields on a Network: Modelling, Statistics, and Applications, Probability and Its Applications, 1995. ,
Signals and Systems, 1983. ,
Principles of Signals and Systems: Deterministic Signals, 1988. ,
Nested sampling for general Bayesian computation, Bayesian Analysis, vol.1, issue.4, 2006. ,
DOI : 10.1214/06-BA127
URL : http://doi.org/10.1214/06-ba127
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995. ,
DOI : 10.1093/biomet/82.4.711
Bayesian model choice: Asymptotics and exact calculations, J. Roy. Statist. Soc. B, vol.56, issue.3, pp.501-514, 1994. ,
DOI : 10.21236/ADA269067
URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a269067.pdf
Approximate Bayesian inference with the weighted likelihood bootstrap, J. Roy. Stat. Soc. B, vol.56, issue.1, pp.3-48, 1994. ,
On the convergence of moments of geometric and harmonic means, Statistica Neerlandica, vol.53, issue.1, pp.96-110, 1999. ,
DOI : 10.1111/1467-9574.00100
Estimating the integrated likelihood via posterior simulation using the harmonic mean identity, Bayesian Statist, pp.1-45, 2007. ,
Bayes Factors, Journal of the American Statistical Association, vol.2, issue.430, pp.773-795, 1995. ,
DOI : 10.1214/ss/1177013241
Monte Carlo Statistical Methods, 2004. ,
Markov Chains for Exploring Posterior Distributions, The Annals of Statistics, vol.22, issue.4, pp.1701-1762, 1994. ,
DOI : 10.1214/aos/1176325750
URL : http://doi.org/10.1214/aos/1176325750
Monte-Carlo Statistical Methods, ser. Springer Texts in Statistics, 2004. ,
Bayesian estimation of regularization and point spread function parameters for Wiener???Hunt deconvolution, Journal of the Optical Society of America A, vol.27, issue.7, pp.1593-1607, 2010. ,
DOI : 10.1364/JOSAA.27.001593
URL : https://hal.archives-ouvertes.fr/hal-00674508
Bayesian Texture and Instrument Parameter Estimation From Blurred and Noisy Images Using MCMC, IEEE Signal Processing Letters, vol.21, issue.6, pp.707-711, 2014. ,
DOI : 10.1109/LSP.2014.2313274
URL : https://hal.archives-ouvertes.fr/hal-00975094