Joint compressive sampling and deconvolution in ultrasound medical imaging, 2015 IEEE International Ultrasonics Symposium (IUS), pp.1-4, 2015. ,
DOI : 10.1109/ULTSYM.2015.0156
URL : https://hal.archives-ouvertes.fr/hal-01363371
Compressive sensing for ultrasound RF echoes using a-Stable Distributions, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp.4304-4307, 2010. ,
DOI : 10.1109/IEMBS.2010.5626210
Frequency Domain Compressive Sampling for Ultrasound Imaging, Advances in Acoustics and Vibration, vol.15, issue.2, p.231317, 2012. ,
DOI : 10.1109/58.139123
URL : http://doi.org/10.1155/2012/231317
Fourier-domain beamforming: the path to compressed ultrasound imaging, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol.61, issue.8, pp.1252-1267, 2014. ,
DOI : 10.1109/TUFFC.2014.3032
Compressive sensing in medical ultrasound, 2012 IEEE International Ultrasonics Symposium, pp.1-6, 2012. ,
DOI : 10.1109/ULTSYM.2012.0486
URL : https://hal.archives-ouvertes.fr/hal-00830731
Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing, Ultrasonics, vol.53, issue.2, pp.525-533, 2013. ,
DOI : 10.1016/j.ultras.2012.09.008
URL : https://hal.archives-ouvertes.fr/hal-00798017
Pulse-echo ultrasound imaging combining compressed sensing and the fast multipole method, 2014 IEEE International Ultrasonics Symposium, pp.2205-2208, 2014. ,
DOI : 10.1109/ULTSYM.2014.0549
Time domain compressive beam forming of ultrasound signals, The Journal of the Acoustical Society of America, vol.137, issue.5, pp.2773-2784, 2015. ,
DOI : 10.1121/1.4919302
Compressed Sensing Reconstruction of 3D Ultrasound Data Using Dictionary Learning and Line-Wise Subsampling, IEEE Transactions on Medical Imaging, vol.34, issue.12, pp.2467-2477, 2015. ,
DOI : 10.1109/TMI.2015.2442154
URL : https://hal.archives-ouvertes.fr/hal-01180189
Blood Velocity Estimation Using Compressive Sensing, IEEE Transactions on Medical Imaging, vol.32, issue.11, pp.1979-1988, 2013. ,
DOI : 10.1109/TMI.2013.2266871
URL : https://hal.archives-ouvertes.fr/hal-00839213
Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006. ,
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol.52, issue.2, pp.489-509, 2006. ,
DOI : 10.1109/TIT.2005.862083
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
Deblurring from highly incomplete measurements for remote sensing, IEEE Trans. Geosci. Remote Sens, vol.47, issue.3, pp.792-802, 2009. ,
Compounded Regularization and Fast Algorithm for Compressive Sensing Deconvolution, 2011 Sixth International Conference on Image and Graphics, pp.616-621, 2011. ,
DOI : 10.1109/ICIG.2011.71
On compressed blind de-convolution of filtered sparse processes, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.4038-4041, 2010. ,
DOI : 10.1109/ICASSP.2010.5495759
Compressive Blind Image Deconvolution, IEEE Transactions on Image Processing, vol.22, issue.10, pp.3994-4006, 2013. ,
DOI : 10.1109/TIP.2013.2266100
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.649.1466
Simultaneous Bayesian compressive sensing and blind deconvolution, Proc. IEEE 20th Eur. Signal Process. Conf. (EUSIPCO), pp.1414-1418, 2012. ,
Compressive Deconvolution in Random Mask Imaging, IEEE Transactions on Computational Imaging, vol.1, issue.4, pp.236-246, 2015. ,
DOI : 10.1109/TCI.2015.2485941
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
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
Reconstruction of Ultrasound RF Echoes Modeled as Stable Random Variables, IEEE Transactions on Computational Imaging, vol.1, issue.2, pp.86-95, 2015. ,
DOI : 10.1109/TCI.2015.2463257
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Foundations and Trends?? in Machine Learning, vol.3, issue.1, pp.1-122, 2011. ,
DOI : 10.1561/2200000016
Deblurring Poissonian images by split Bregman techniques, Journal of Visual Communication and Image Representation, vol.21, issue.3, pp.193-199, 2010. ,
DOI : 10.1016/j.jvcir.2009.10.006
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.564.8327
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
Applications of Lagrangian-based alternating direction methods and connections to split Bregman, CAM Rep, vol.9, p.31, 2009. ,
Solving Constrained Total-variation Image Restoration and Reconstruction Problems via Alternating Direction Methods, SIAM Journal on Scientific Computing, vol.32, issue.5, pp.2710-2736, 2010. ,
DOI : 10.1137/090774823
A parallel inertial proximal optimization method, Pacific J. Optim, vol.8, issue.2, pp.273-305, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00790702
Parallel Proximal Algorithm for Image Restoration Using Hybrid Regularization, IEEE Transactions on Image Processing, vol.20, issue.9, pp.2450-2462, 2011. ,
DOI : 10.1109/TIP.2011.2128335
URL : https://hal.archives-ouvertes.fr/hal-00826121
Relaxing Tight Frame Condition in Parallel Proximal Methods for Signal Restoration, IEEE Transactions on Signal Processing, vol.60, issue.2, pp.968-973, 2012. ,
DOI : 10.1109/TSP.2011.2173684
URL : https://hal.archives-ouvertes.fr/hal-00692256
Proximal splitting methods in signal processing, " in Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp.185-212, 2011. ,
Ultrasound compressive deconvolution with ? p -norm prior, Proc. IEEE 23rd Eur. Signal Process. Conf. (EUSIPCO), pp.2791-2795, 2015. ,
DOI : 10.1109/eusipco.2015.7362893
Group sparse optimization by alternating direction method, Proc. SPIE, p.88580, 2013. ,
Wavelet restoration of medical pulse-echo ultrasound images in an EM framework, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.54, issue.3, pp.550-568, 2007. ,
DOI : 10.1109/TUFFC.2007.278
A model for the propagation and scattering of ultrasound in tissue, The Journal of the Acoustical Society of America, vol.89, issue.1, pp.182-190, 1991. ,
DOI : 10.1121/1.400497
Fast and Efficient Compressive Sensing Using Structurally Random Matrices, IEEE Transactions on Signal Processing, vol.60, issue.1, pp.139-154, 2012. ,
DOI : 10.1109/TSP.2011.2170977
URL : http://arxiv.org/abs/1106.5037
A novel approach to the 2-D blind deconvolution problem in medical ultrasound, IEEE Transactions on Medical Imaging, vol.24, issue.1, pp.86-104, 2005. ,
DOI : 10.1109/TMI.2004.838326
A blind deconvolution approach to ultrasound imaging, IEEE Trans. Ultrason., Ferroelectr., Freq. Control, vol.59, issue.2, pp.271-280, 2012. ,
Empirical Mode Decomposition as a Filter Bank, IEEE Signal Processing Letters, vol.11, issue.2, pp.112-114, 2004. ,
DOI : 10.1109/LSP.2003.821662
URL : https://hal.archives-ouvertes.fr/inria-00570615
Design of Optimal 2-D Nongrid Sparse Arrays for Medical Ultrasound, IEEE Transactions on Biomedical Engineering, vol.60, issue.11, pp.3093-3102, 2013. ,
DOI : 10.1109/TBME.2013.2267742
URL : https://hal.archives-ouvertes.fr/hal-00850032
Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing, SIAM Journal on Scientific Computing, vol.33, issue.1, pp.250-278, 2011. ,
DOI : 10.1137/090777761
Expectation Maximization for Joint Deconvolution and Statistics Estimation, Acoustical Imaging. The Netherlands, pp.335-343, 2011. ,
DOI : 10.1007/978-90-481-3255-3_38