Compressed Sensing Approaches for Polynomial Approximation of High-Dimensional Functions, Compressed Sensing and its Applications, pp.93-124, 2017. ,
DOI : 10.1016/j.jcp.2013.04.004
BREAKING THE COHERENCE BARRIER: A NEW THEORY FOR COMPRESSED SENSING, Forum of Mathematics, Sigma, vol.94840, 2017. ,
DOI : 10.1017/S0962492900002816
The Quest for Optimal Sampling: Computationally Efficient, Structure-Exploiting Measurements for Compressed Sensing, Compressed Sensing and its Applications, 2014. ,
DOI : 10.1007/978-3-319-16042-9_5
URL : http://arxiv.org/pdf/1403.6540
A Note on Compressed Sensing of Structured Sparse Wavelet Coefficients From Subsampled Fourier Measurements, IEEE Signal Processing Letters, vol.23, issue.5, pp.732-736, 2016. ,
DOI : 10.1109/LSP.2016.2550101
URL : http://arxiv.org/pdf/1403.6541
Living on the edge: Phase transitions in convex programs with random data. Information and Inference: A, Journal of the IMA, vol.3, issue.3, pp.224-294, 2014. ,
DOI : 10.1093/imaiai/iau005
URL : https://academic.oup.com/imaiai/article-pdf/3/3/224/2110023/iau005.pdf
An Analysis of Block Sampling Strategies in Compressed Sensing, IEEE Transactions on Information Theory, vol.62, issue.4, 2014. ,
DOI : 10.1109/TIT.2016.2524628
URL : https://hal.archives-ouvertes.fr/hal-00823711
Compressed sensing with structured sparsity and structured acquisition, Applied and Computational Harmonic Analysis, 2017. ,
DOI : 10.1016/j.acha.2017.05.005
URL : https://hal.archives-ouvertes.fr/hal-01149456
Compressed sensing and parallel acquisition, IEEE Transactions on Information Theory, 2017. ,
Variable Density Sampling with Continuous Trajectories, SIAM Journal on Imaging Sciences, vol.7, issue.4, pp.1962-1992, 2014. ,
DOI : 10.1137/130946642
URL : https://hal.archives-ouvertes.fr/hal-00908486
Variable density compressed sensing in MRI. Theoretical vs heuristic sampling strategies, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.298-301, 2013. ,
DOI : 10.1109/ISBI.2013.6556471
URL : https://hal.archives-ouvertes.fr/hal-00848271
Polynomial approximation via compressed sensing of high-dimensional functions on lower sets, Mathematics of Computation, 2017. ,
DOI : 10.1090/mcom/3272
URL : http://arxiv.org/pdf/1602.05823
A probabilistic and ripless theory of compressed sensing. Information Theory, IEEE Transactions on, vol.57, issue.11, pp.7235-7254, 2011. ,
Sparsity and incoherence in compressive sampling, Inverse Problems, vol.23, issue.3, p.969, 2007. ,
DOI : 10.1088/0266-5611/23/3/008
A mathematical introduction to compressive sensing, 2013. ,
DOI : 10.1007/978-0-8176-4948-7
Stable and Robust Sampling Strategies for Compressive Imaging, IEEE Transactions on Image Processing, vol.23, issue.2, pp.612-622, 2014. ,
DOI : 10.1109/TIP.2013.2288004
URL : http://arxiv.org/pdf/1210.2380
Compressed sensing with local structure: Uniform recovery guarantees for the sparsity in levels class, Applied and Computational Harmonic Analysis, p.2017 ,
DOI : 10.1016/j.acha.2017.05.006
URL : http://arxiv.org/pdf/1601.01988
Optimum linear array for aperture synthesis imaging based on redundant spacing calibration, Optical Engineering, vol.53, issue.5, p.53109, 2014. ,
DOI : 10.1117/1.OE.53.5.053109
On Variable Density Compressive Sampling, IEEE Signal Processing Letters, vol.18, issue.10, pp.595-598, 2011. ,
DOI : 10.1109/LSP.2011.2163712
URL : https://infoscience.epfl.ch/record/165480/files/double.pdf
Compressed sensing of ultrasound images: Sampling of spatial and frequency domains, 2010 IEEE Workshop On Signal Processing Systems, pp.231-236, 2010. ,
DOI : 10.1109/SIPS.2010.5624793
Sparse Legendre expansions via <mml:math altimg="si12.gif" display="inline" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:msub><mml:mrow><mml:mi>???</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-minimization, Journal of Approximation Theory, vol.164, issue.5, pp.517-533, 2012. ,
DOI : 10.1016/j.jat.2012.01.008
Convex Recovery of a Structured Signal from Independent Random Linear Measurements, Sampling Theory, a Renaissance, pp.67-101, 2015. ,
DOI : 10.1007/978-3-319-19749-4_2
URL : http://arxiv.org/pdf/1405.1102
SPGL1: A solver for large-scale sparse reconstruction, 2007. ,
Probing the Pareto Frontier for Basis Pursuit Solutions, SIAM Journal on Scientific Computing, vol.31, issue.2, pp.890-912, 2008. ,
DOI : 10.1137/080714488
Description of parallel imaging in MRI using multiple coils, Magnetic Resonance in Medicine, vol.29, issue.3, pp.495-499, 2000. ,
DOI : 10.1002/1522-2594(200009)44:3<495::AID-MRM23>3.0.CO;2-S