E. Macé, G. Montaldo, I. Cohen, M. Baulac, M. Fink et al., Functional ultrasound imaging of the brain, Nature Methods, vol.12, issue.8, pp.662-664, 2011.
DOI : 10.1038/nmeth.1530

X. C. Wortsman, E. A. Holm, H. C. Wulf, and G. B. Jemec, Real-time spatial compound ultrasound imaging of skin, Skin Research and Technology, vol.4, issue.1, pp.23-31, 2004.
DOI : 10.1046/j.1365-2133.2000.03652.x

J. A. Jensen, S. I. Nikolov, K. L. Gammelmark, and M. H. Pedersen, Synthetic aperture ultrasound imaging, Ultrasonics, vol.44, pp.5-15, 2006.
DOI : 10.1016/j.ultras.2006.07.017

F. Viola and W. F. Walker, Adaptive signal processing in medical ultrasound beamforming, IEEE Ultrasonics Symposium, 2005., pp.1980-1983, 2005.
DOI : 10.1109/ULTSYM.2005.1603264

O. M. Rindal, J. P. Åsen, S. Holm, and A. Austeng, Understanding contrast improvements from capon beamforming, 2014 IEEE International Ultrasonics Symposium, pp.1694-1697, 2014.
DOI : 10.1109/ULTSYM.2014.0420

W. Chen, Y. Zhao, and J. Gao, Improved capon beamforming algorithm by using inverse covariance matrix calculation, Proc. IET Int. Radar Conf, pp.1-6, 2013.

K. Kim, S. Park, J. Kim, S. Park, and M. Bae, A fast minimum variance beamforming method using principal component analysis, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol.61, issue.6, pp.930-945, 2014.
DOI : 10.1109/TUFFC.2014.2989

J. Synnevåg, A. Austeng, and S. Holm, Adaptive Beamforming Applied to Medical Ultrasound Imaging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.54, issue.8, pp.1606-1613, 2007.
DOI : 10.1109/TUFFC.2007.431

B. M. Asl and A. Mahloojifar, Minimum variance beamforming combined with adaptive coherence weighting applied to medical ultrasound imaging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.56, issue.9, pp.1923-1931, 2009.
DOI : 10.1109/TUFFC.2009.1268

C. C. Nilsen and I. Hafizovic, Beamspace adaptive beamforming for ultrasound imaging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.56, issue.10, pp.2187-2197, 2009.
DOI : 10.1109/TUFFC.2009.1301

A. C. Jensen and A. Austeng, An approach to multibeam covariance matrices for adaptive beamforming in ultrasonography, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.59, issue.6, pp.1139-1148, 2012.
DOI : 10.1109/TUFFC.2012.2304

A. C. Jensen and A. Austeng, The iterative adaptive approach in medical ultrasound imaging, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol.61, issue.10, pp.1688-1697, 2014.
DOI : 10.1109/TUFFC.2014.006478

T. Yardibi, J. Li, and P. Stoica, Nonparametric and sparse signal representations in array processing via iterative adaptive approaches, 2008 42nd Asilomar Conference on Signals, Systems and Computers, pp.278-282, 2008.
DOI : 10.1109/ACSSC.2008.5074408

O. Michailovich and A. Tannenbaum, Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach, IEEE Transactions on Image Processing, vol.16, issue.12, pp.3005-3019, 2007.
DOI : 10.1109/TIP.2007.910179

R. Jirik and T. Taxt, Two-dimensional blind Bayesian deconvolution of medical ultrasound images, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.55, issue.10, pp.2140-2153, 2008.
DOI : 10.1109/TUFFC.914

R. Lavarello, F. Kamalabadi, and W. D. O-'brien, A regularized inverse approach to ultrasonic pulse-echo imaging, IEEE Transactions on Medical Imaging, vol.25, issue.6, pp.712-722, 2006.
DOI : 10.1109/TMI.2006.873297

F. Lingvall, T. Olofsson-]-b, and F. C. Madore, On Time-Domain Model-Based Ultrasonic Array Imaging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.54, issue.8, pp.1623-1633, 2007.
DOI : 10.1109/TUFFC.2007.433

F. Viola, M. A. Ellis, and W. F. Walker, Time-Domain Optimized Near-Field Estimator for Ultrasound Imaging: Initial Development and Results, IEEE Transactions on Medical Imaging, vol.27, issue.1, pp.99-110, 2008.
DOI : 10.1109/TMI.2007.903579

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2850070

Y. Wan and E. S. Ebbini, 2C-1 A Post-Beamforming 2D Pseudoinverse Filter for Coarsely Sampled Ultrasound Arrays, 2007 IEEE Ultrasonics Symposium Proceedings, pp.1888-1902, 2009.
DOI : 10.1109/ULTSYM.2007.19

A. Tuysuzoglu, J. M. Kracht, R. O. Cleveland, M. Çetin, and W. C. , Sparsity driven ultrasound imaging, The Journal of the Acoustical Society of America, vol.131, issue.2, pp.1271-12813675002, 1121.
DOI : 10.1121/1.3675002

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3292603

M. A. Ellis, F. Viola, and W. F. Walker, Super-Resolution Image Reconstruction Using Diffuse Source Models, Ultrasound in Medicine & Biology, vol.36, issue.6, pp.967-977, 2010.
DOI : 10.1016/j.ultrasmedbio.2010.03.002

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2878910

J. A. Jensen, Deconvolution of Ultrasound Images, Ultrasonic Imaging, vol.10, issue.1, pp.1-15, 1992.
DOI : 10.1177/016173469201400101

N. Zhao, A. Basarab, D. Kouamé, and J. Tourneret, Joint Bayesian deconvolution and pointspread function estimation for ultrasound imaging, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp.235-238, 2015.
DOI : 10.1109/ISBI.2015.7163857

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005.
DOI : 10.1073/pnas.201162998

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.4696

Z. Chen, A. Basarab, and D. Kouamé, Compressive Deconvolution in Medical Ultrasound Imaging, IEEE Transactions on Medical Imaging, vol.35, issue.3, pp.728-737, 2016.
DOI : 10.1109/TMI.2015.2493241

URL : https://hal.archives-ouvertes.fr/hal-01363371

O. Michailovich and Y. Rathi, Adaptive learning of tissue reflectivity statistics and its application to deconvolution of medical ultrasound scans, 2015 IEEE International Ultrasonics Symposium (IUS), pp.1-4, 2015.
DOI : 10.1109/ULTSYM.2015.0492

D. Malioutov, M. Çetin, and A. S. Willsky, A sparse signal reconstruction perspective for source localization with sensor arrays, IEEE Transactions on Signal Processing, vol.53, issue.8, pp.3010-3022, 2005.
DOI : 10.1109/TSP.2005.850882

J. Fuchs, Linear programming in spectral estimation. Application to array processing, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, pp.3161-3164, 1996.
DOI : 10.1109/ICASSP.1996.550547

L. Du, T. Yardibi, J. Li, and P. Stoica, Review of user parameter-free robust adaptive beamforming algorithms, Proc. 42nd Asilomar Conf. Signals, Syst. Comput, pp.363-367, 2008.

C. Yu, C. Zhang, and L. Xie, A blind deconvolution approach to ultrasound imaging, IEEE Trans. Ultrason., Ferroelect., Freq. Control, vol.59, issue.2, pp.271-280, 2012.

S. S. Chen, D. L. Donoho, and M. A. Saunders, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1998.
DOI : 10.1137/S1064827596304010

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.7694

D. A. Lorenz, M. E. Pfetsch, and A. M. Tillmann, Available: ht Solving basis pursuit: Heuristic optimality check and solver comparison, Download YALL1: Your ALgorithms for L1, 2015.
DOI : 10.1145/2689662

J. Huang, C. R. Berger, S. Zhou, and J. Huang, Comparison of basis pursuit algorithms for sparse channel estimation in underwater acoustic OFDM, OCEANS'10 IEEE SYDNEY, pp.1-6, 2010.
DOI : 10.1109/OCEANSSYD.2010.5603522

A. Tikhonov, Solution of incorrectly formulated problems and the regularization method, Sov. Math. Dokl, vol.5, pp.1035-1038, 1963.

J. A. Jensen and N. B. Svendsen, Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.39, issue.2, pp.262-267, 1992.
DOI : 10.1109/58.139123

J. A. Jensen, Simulation of advanced ultrasound systems using Field II, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), pp.636-639, 2004.
DOI : 10.1109/ISBI.2004.1398618

M. Alessandrini, H. Liebgott, D. Friboulet, and O. Bernard, Simulation of realistic echocardiographic sequences for ground-truth validation of motion estimation, 2012 19th IEEE International Conference on Image Processing, pp.2329-2332, 2012.
DOI : 10.1109/ICIP.2012.6467363

URL : https://hal.archives-ouvertes.fr/hal-00830363

Z. Chen, A. Basarab, and D. Kouamé, A simulation study on the choice of regularization parameter in ? 2 -norm ultrasound image restoration, Proc. 37th Annu. Int. Conf, pp.6346-6349, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01371780

N. P. Galatsanos and A. K. Katsaggelos, Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation, IEEE Transactions on Image Processing, vol.1, issue.3, pp.322-336, 1992.
DOI : 10.1109/83.148606

S. Ramani, Z. Liu, J. Rosen, J. Nielsen, and J. A. Fessler, 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

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3411925

J. Garayoa and P. Castro, A study on image quality provided by a kilovoltage cone-beam computed tomography, Journal of Applied Clinical Medical Physics, vol.217, issue.3
DOI : 10.1120/jacmp.v14i1.3888

. Med and . Phys, Available: ht, p.2473888, 2013.

T. Taxt and J. Strand, Two-dimensional noise-robust blind deconvolution of ultrasound images, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.48, issue.4, pp.861-866, 2001.
DOI : 10.1109/58.935701

M. Alessandrini, A restoration framework for ultrasonic tissue characterization, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.58, issue.11, pp.2344-2360, 2011.
DOI : 10.1109/TUFFC.2011.2092

URL : https://hal.archives-ouvertes.fr/hal-00688924

N. Zhao, A. Basarab, D. Kouamé, and J. Tourneret, Restoration of ultrasound images using a hierarchical Bayesian model with a generalized Gaussian prior, 2014 IEEE International Conference on Image Processing (ICIP), pp.4577-4581, 2014.
DOI : 10.1109/ICIP.2014.7025928

URL : https://hal.archives-ouvertes.fr/hal-01399870

T. Szasz, A. Basarab, M. Vaida, and D. Kouamé, Elastic-net based beamforming in medical ultrasound imaging, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp.477-480, 2016.
DOI : 10.1109/ISBI.2016.7493311

J. Capon, High-resolution frequency-wavenumber spectrum analysis, Proc. IEEE, pp.1408-1418, 1969.
DOI : 10.1109/PROC.1969.7278

J. Synnevåg, A. Austeng, and S. Holm, Benefits of minimum-variance beamforming in medical ultrasound imaging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.56, issue.9, pp.1868-1879, 2009.
DOI : 10.1109/TUFFC.2009.1263

G. M. Kautz and M. D. Zoltowski, Beamspace DOA estimation featuring multirate eigenvector processing, IEEE Transactions on Signal Processing, vol.44, issue.7, pp.1765-1778, 1996.
DOI : 10.1109/78.510623