An analysis of block sampling strategies in compressed sensing, IEEE transactions on information theory, vol.62, pp.2125-2139, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-00823711
Gpu accelerated greedy algorithms for compressed sensing, Mathematical Programming Computation, vol.5, pp.267-304, 2013. ,
Iterative hard thresholding for compressed sensing, Applied and computational harmonic analysis, vol.27, pp.265-274, 2009. ,
The restricted isometry property and its implications for compressed sensing, Comptes rendus mathematique, vol.346, pp.589-592, 2008. ,
Curvelets: A surprisingly effective nonadaptive representation for objects with edges, 2000. ,
A probabilistic and ripless theory of compressed sensing, IEEE transactions on information theory, vol.57, pp.7235-7254, 2011. ,
Near-ideal model selection by ? 1 minimization, The Annals of Statistics, vol.37, pp.2145-2177, 2009. ,
Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on information theory, vol.52, pp.489-509, 2006. ,
Stable signal recovery from incomplete and inaccurate measurements, Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, vol.59, pp.1207-1223, 2006. ,
Variable density sampling with continuous trajectories, SIAM J. Imaging Sciences, vol.7, pp.1962-1992, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-00908486
Spectral domain optical coherence tomography: ultra-high speed, ultra-high resolution ophthalmic imaging, Archives of ophthalmology, vol.123, pp.1715-1720, 2005. ,
Wavelet denoising during optical coherence tomography of the prostate nerves using the complex wavelet transform, p.30, 2008. ,
, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3016-3019
Sensitivity advantage of swept source and fourier domain optical coherence tomography, Optics express, vol.11, pp.2183-2189, 2003. ,
Sparse recovery with unknown variance: a lasso-type approach, IEEE Transactions on Information Theory, vol.60, pp.3970-3988, 2014. ,
Compressed sensing and best fc-term approximation, Journal of the American mathematical society, vol.22, pp.211-231, 2009. ,
Introduction to compressed sensing. chapter 1 of compressed sensing: Theory and applications, 2012. ,
Compressed sensing, IEEE Transactions on information theory, vol.52, pp.1289-1306, 2006. ,
URL : https://hal.archives-ouvertes.fr/inria-00369486
State-of-the-art retinal optical coherence tomography, Progress in retinal and eye research, vol.27, pp.45-88, 2008. ,
Shearlet transform: A good candidate for compressed sensing in optical coherence tomography, IEEE 38th Annual International Conference of the, IEEE, pp.435-438, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01355488
Testing the nullspace property using semidefinite programming, Mathematical programming, vol.127, pp.123-144, 2011. ,
Medical image fusion based on feature extraction and sparse representation, International journal of biomedical imaging, 2017. ,
A mathematical introduction to compressive sensing, 2013. ,
,
Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy, Neoplasia, 2000. ,
On verifiable sufficient conditions for sparse signal recovery via ? 1 minimization, Mathematical programming, vol.127, pp.57-88, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00321775
Construction of compactly supported shearlet frames, Constructive Approximation, vol.35, 2012. ,
Introduction to shearlets, pp.1-38, 2012. ,
, Shearlab 3d: Faithful digital shearlet transforms based on compactly supported shearlets, 2014.
Sparse multidimensional representation using shearlets, Wavelets XI, International Society for Optics and Photonics, p.59140, 2005. ,
Compressive sd-oct: the application of compressed sensing in spectral domain optical coherence tomography, Opt. Express, vol.18, pp.22010-22019, 2010. ,