B. Adcock, S. Brugiapaglia, and C. G. Webster, 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

B. Adcock, A. C. Hansen, C. Poon, and B. Roman, BREAKING THE COHERENCE BARRIER: A NEW THEORY FOR COMPRESSED SENSING, Forum of Mathematics, Sigma, vol.94840, 2017.
DOI : 10.1017/S0962492900002816

[. Adcock, A. C. Hansen, and B. Roman, 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

[. Adcock, A. C. Hansen, and B. Roman, 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

D. Amelunxen, M. Lotz, B. Michael, J. A. Mccoy, and . Tropp, 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

J. Bigot, C. Boyer, and P. Weiss, 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

C. Boyer, J. Bigot, and P. Weiss, 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

I. Yong, C. , and B. Adcock, Compressed sensing and parallel acquisition, IEEE Transactions on Information Theory, 2017.

[. Chauffert, P. Ciuciu, J. Kahn, and P. Weiss, 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

N. Chauffert, P. Ciuciu, and P. Weiss, 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

A. Chkifa, N. Dexter, H. Tran, and C. G. Webster, 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

E. Candès and Y. Plan, A probabilistic and ripless theory of compressed sensing. Information Theory, IEEE Transactions on, vol.57, issue.11, pp.7235-7254, 2011.

E. Candès and J. Romberg, Sparsity and incoherence in compressive sampling, Inverse Problems, vol.23, issue.3, p.969, 2007.
DOI : 10.1088/0266-5611/23/3/008

S. Foucart and H. Rauhut, A mathematical introduction to compressive sensing, 2013.
DOI : 10.1007/978-0-8176-4948-7

F. Krahmer and R. Ward, 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

C. Li and B. Adcock, 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

L. Liu, Y. He, J. Zhang, H. Jia, and J. Ma, 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

G. Puy, P. Vandergheynst, and Y. Wiaux, 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

C. Quinsac, A. Basarab, J. Girault, and D. Kouamé, 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

H. Rauhut and R. Ward, 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

J. A. Tropp, 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

M. P. Berg and . Friedlander, SPGL1: A solver for large-scale sparse reconstruction, 2007.

M. P. Berg and . Friedlander, 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

Y. Wang, 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