. .. -oct, Existing Compressed Sensing approaches in SD, p.135

, CS-based SD-OCT image simplification and segmentation, p.136

. .. , IV.2.2 FSR-based processing of SD-OCT images, p.138

, Simplification and segmentation results

, Development and implementation of a CS-based SS-OCT system 152

, Design of a CS-based SS-OCT set-up

. .. , Application to flow velocity measurement, p.166

. Bibliography,

R. Abergel, C. Louchet, L. Moisan, and T. Zeng, Total variation restoration of images corrupted by Poisson noise with iterated conditional expectations, International Conference on Scale Space and Variational Methods in Computer Vision, pp.178-190, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01275813

R. Abergel, Several mathematical models and fast algorithms for image processing, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01734541

B. Adcock, C. Anders, C. Hansen, B. Poon, and . Roman, Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing, 2013.

B. Adcock, C. Anders, C. Hansen, B. Poon, and . Roman, Breaking the coherence barrier: A new theory for compressed sensing, Forum of Mathematics, vol.5, 2017.

M. Aharon, M. Elad, and A. Bruckstein, rmk-svd: An algorithm for designing overcomplete dictionaries for sparse representation, IEEE Transactions on signal processing, vol.54, issue.11, pp.4311-4322, 2006.

M. Etienne, . Aliot, G. William, J. Stevenson, F. Ma-almendral-garrote et al., Ehra/hrs expert consensus on catheter ablation of ventricular arrhythmias: developed in a partnership with the european heart rhythm association (ehra), a registered branch of the european society of cardiology (esc), and the heart rhythm society (hrs)

, Europace, vol.11, issue.6, pp.771-817, 2009.

N. Christina-m-ambrosi, . Moazami, M. Andrew, I. R. Rollins, and . Efimov, Virtual histology of the human heart using optical coherence tomography, Journal of Biomedical optics, vol.14, issue.5, p.54002, 2009.

C. V. Angelino, E. Debreuve, and M. Barlaud, Patch confidence knearest neighbors denoising, IEEE International Conference on Image Processing (ICIP), pp.1129-1132, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00518078

D. Axelrod, L. Nancy, T. P. Thompson, and . Burghardt, Total internal reflection fluorescent microscopy, Journal of Microscopy, vol.129, issue.1, pp.19-28, 1983.

R. Ayuk, H. Giovannini, A. Jost, J. Mudry, T. Girard et al., Structured illumination fluorescence microscopy with distorted excitations using a filtered blind-sim algorithm, Optics Letters, vol.38, issue.22, pp.4723-4726, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00932681

A. Azioune, N. Carpi, Q. Tseng, M. Thery, and M. Piel, Protein micropatterns: A direct printing protocol using deep uvs, In Methods in Cell Biology, vol.97, pp.133-146
URL : https://hal.archives-ouvertes.fr/hal-00981469

F. Balzarotti, Y. Eilers, C. Klaus, . Gwosch, H. Arvid et al., Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes, Science, vol.355, issue.6325, pp.606-612, 2017.

L. Bao, M. Robini, W. Y. Liu, and Y. Zhu, Structure-adaptive sparse denoising for diffusion-tensor MRI, Medical Image Analysis, vol.17, issue.4, pp.442-457, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00850585

G. Richard and . Baraniuk, Compressive sensing [lecture notes, IEEE signal processing magazine, vol.24, issue.4, pp.118-121, 2007.

M. Bates, B. Huang, T. Graham, X. Dempsey, and . Zhuang, Multicolor super-resolution imaging with photo-switchable fluorescent probes, Science, vol.317, issue.5845, pp.1749-1753, 2007.

M. Bayleyegn, Development of optical coherence tomography for monitoring the glaucoma laser surgery, 2012.
URL : https://hal.archives-ouvertes.fr/tel-00816669

E. Beaurepaire, C. Boccara, M. Lebec, H. Blanchot, and . Saint-jalmes, Full-field optical coherence microscopy, Optics Letters, vol.23, issue.4, pp.244-246, 1998.
URL : https://hal.archives-ouvertes.fr/hal-02372514

A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.

S. Becker, J. Bobin, and E. Candès, Nesta: A fast and accurate firstorder method for sparse recovery, Journal on Imaging Sciences, vol.4, issue.1, pp.1-39, 2011.

E. Betzig, H. George, R. Patterson, W. Sougrat, S. Lindwasser et al., Imaging intracellular fluorescent proteins at nanometer resolution, Science, vol.313, issue.5793, pp.1642-1645, 2006.

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, pp.2125-2139, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02505914

A. André, N. Bindilatti, and . Da-mascarenhas, A nonlocal poisson denoising algorithm based on stochastic distances, IEEE Signal Processing Letters, vol.20, issue.11, pp.1010-1013, 2013.

A. André, . Bindilatti, A. C. Marcelo, N. Vieira, and . Mascarenhas, Poisson wiener filtering with non-local weighted parameter estimation using stochastic distances, Signal Processing, vol.144, pp.68-76, 2018.

G. Binnig, C. Calvin-f-quate, and . Gerber, Atomic force microscope, Physical Review Letters, vol.56, issue.9, p.930, 1986.

J. M. Bioucas-dias and M. A. Figueiredo, Multiplicative noise removal using variable splitting and constrained optimization, IEEE Transactions on Image Processing, vol.19, issue.7, pp.1720-1730, 2010.

J. Bobin, J. Starck, and R. Ottensamer, Compressed sensing in astronomy, IEEE Journal of Selected Topics in Signal Processing, vol.2, issue.5, pp.718-726, 2008.

P. Bofill and M. Zibulevsky, Blind separation of more sources than mixtures using sparsity of their short-time fourier transform, Proc. ica, pp.87-92, 2000.

A. Stephen, W. Boppart, . Luo, L. Daniel, K. W. Marks et al., Optical coherence tomography: feasibility for basic research and imageguided surgery of breast cancer, Breast cancer research and treatment, vol.84, issue.2, pp.85-97, 2004.

A. K. Boyat and B. K. Joshi, A review paper: noise models in digital image processing, An International Journal, vol.6, issue.2, pp.63-75, 2015.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Machine Learning, vol.3, pp.1-122, 2011.

J. David, K. Brady, . Choi, L. Daniel, R. Marks et al., Compressive holography. Optics Express, vol.17, issue.15, pp.13040-13049, 2009.

A. Buades, B. Coll, and J. Morel, A review of image denoising algorithms, with a new one, Multiscale Modeling and Simulation, vol.4, issue.2, pp.490-530, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00271141

S. Andrew, M. Camp, . Ruggeri, C. Gustavo, M. L. Munguba et al., Structural correlation between the nerve fiber layer and retinal ganglion cell loss in mice with targeted disruption of the brn3b gene, Investigative Ophthalmology & Visual Science, vol.52, issue.8, pp.5226-5232, 2011.

J. Emmanuel, D. L. Candès, and . Donoho, New tight frames of curvelets and optimal representations of objects with piecewise c2 singularities, Communications on Pure and Applied Mathematics, vol.57, issue.2, pp.219-266, 2004.

E. Candes and J. Romberg, 1 -magic: Recovery of sparse signals via convex programming, vol.4, p.14, 2005.

J. Emmanuel, T. Candes, and . Tao, Decoding by linear programming, IEEE Transactions on Information Theory, vol.51, issue.12, pp.4203-4215, 2005.

E. J. Candès, J. Romberg, and T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol.52, issue.2, pp.489-509, 2006.

E. J. Candès, J. Romberg, and T. Tao, Stable signal recovery from incomplete and inaccurate measurements, Communications on Pure and Applied Mathematics, vol.59, issue.8, pp.1207-1223, 2006.

J. Emmanuel, T. Candes, and . Tao, Near-optimal signal recovery from random projections: Universal encoding strategies?, IEEE Transactions on Information Theory, vol.52, issue.12, pp.5406-5425, 2006.

E. Candes and J. Romberg, Sparsity and incoherence in compressive sampling, Inverse Problems, vol.23, issue.3, p.969, 2007.

J. Emmanuel, Y. C. Candes, D. Eldar, P. Needell, and . Randall, Compressed sensing with coherent and redundant dictionaries, Applied and Computational Harmonic Analysis, vol.31, issue.1, pp.59-73, 2011.

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

A. Chambolle, An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, vol.20, issue.1, pp.89-97, 2004.

A. Chambolle, V. Caselles, D. Cremers, M. Novaga, and T. Pock, An introduction to total variation for image analysis, Theoretical Foundations and Numerical Methods for Sparse Recovery, vol.9, p.227, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00437581

A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging, Journal of Mathematical Imaging and Vision, vol.40, issue.1, pp.120-145, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00490826

P. Chatterjee and P. Milanfar, Patch-based near-optimal image denoising, IEEE Transactions on Image Processing, vol.21, issue.4, pp.1635-1649, 2012.

N. 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.
URL : https://hal.archives-ouvertes.fr/hal-00908486

G. Chen, C. Yang, and S. Xie, Gradient-based structural similarity for image quality assessment, IEEE International Conference on Image Processing, pp.2929-2932, 2006.

G. Chen, J. Tang, and S. Leng, Prior image constrained compressed sensing (piccs): a method to accurately reconstruct dynamic ct images from highly undersampled projection data sets, Physics, vol.35, issue.2, pp.660-663, 2008.

. Sr-chinn, J. G. Swanson, and . Fujimoto, Optical coherence tomography using a frequency-tunable optical source, Optics Letters, vol.22, issue.5, pp.340-342, 1997.

. Michael-a-choma, V. Marinko, C. Sarunic, J. A. Yang, and . Izatt, Sensitivity advantage of swept source and fourier domain optical coherence tomography, Optics Express, vol.11, issue.18, pp.2183-2189, 2003.

A. Macleod and C. , Representation of a function by its line integrals, with some radiological applications, Journal of Applied Physics, vol.34, issue.9, pp.2722-2727, 1963.

S. Culley, K. L. Tosheva, P. M. Pereira, and R. Henriques, Srrf: Universal live-cell super-resolution microscopy, The International Journal of Biochemistry & Cell Biology, 2018.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Transactions on Image Processing (TIP), vol.16, issue.8, pp.2080-2095, 2007.

A. Dalalyan and A. B. Tsybakov, Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity, Machine Learning, vol.72, pp.39-61, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00291504

A. Mark, . Davenport, and . Michael-b-wakin, Analysis of orthogonal matching pursuit using the restricted isometry property, IEEE Transactions on Information Theory, vol.56, issue.9, pp.4395-4401, 2010.

J. Boer, B. Cense, H. Park, C. Mark, G. J. Pierce et al., Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography, Optics Letters, vol.28, issue.21, pp.2067-2069, 2003.

M. Delbracio and G. Sapiro, Hand-held video deblurring via efficient fourier aggregation, IEEE Transactions on Computational Imaging, vol.1, issue.4, pp.270-283, 2015.

C. Deledalle, F. Tupin, and L. Denis, Poisson NL means: Unsupervised non local means for Poisson noise, IEEE International Conference on Image Processing (ICIP), pp.801-804, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00957982

J. Demmerle, E. Wegel, L. Schermelleh, and I. M. Dobbie, Assessing resolution in super-resolution imaging, Methods, vol.88, pp.3-10, 2015.

. Michael-l-don, Designing for compressive sensing: Compressive art, camouflage, fonts, and quick response codes, 2018.

L. Donati, M. Nilchian, S. Trépout, C. Messaoudi, S. Marco et al., Compressed sensing for stem tomography, Ultramicroscopy, vol.179, pp.47-56, 2017.

L. David, P. Donoho, and . Stark, Uncertainty principles and signal recovery, SIAM Journal on Applied Mathematics, vol.49, issue.3, pp.906-931, 1989.

D. L. Donoho and I. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika, pp.425-455, 1994.

D. L. Donoho, De-noising by soft-thresholding, IEEE Transactions on Information Theory, vol.41, issue.3, pp.613-627, 1995.

D. L. Donoho and I. M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistical Association, vol.90, issue.432, pp.1200-1224, 1995.

L. David, X. Donoho, and . Huo, Uncertainty principles and ideal atomic decomposition, IEEE Transactions on Information Theory, vol.47, issue.7, pp.2845-2862, 2001.

L. David, M. Donoho, and . Elad, Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization, Proceedings of the National Academy of Sciences, vol.100, issue.5, pp.2197-2202, 2003.

L. David, M. Donoho, V. N. Elad, and . Temlyakov, Stable recovery of sparse overcomplete representations in the presence of noise, IEEE Transactions on Information Theory, vol.52, issue.1, pp.6-18, 2006.

D. L. Donoho, Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00369486

C. Dossal, M. Chabanol, G. Peyré, and J. Fadili, Sharp support recovery from noisy random measurements by 1 -minimization, Applied and Computational Harmonic Analysis, vol.33, issue.1, pp.24-43, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00553670

G. Duarte-carvajalino09]-julio-martin-duarte-carvajalino and . Sapiro, Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization, IEEE Transactions on Image Processing, vol.18, issue.7, pp.1395-1408, 2009.

F. Marco, . Duarte, A. Mark, D. Davenport, J. N. Takhar et al., Single-pixel imaging via compressive sampling, IEEE Signal Processing Magazine, vol.25, issue.2, pp.83-91, 2008.

F. Marco, R. G. Duarte, and . Baraniuk, Spectral compressive sensing, Applied and Computational Harmonic Analysis, vol.35, issue.1, pp.111-129, 2013.

R. O. Duda and P. E. Hart, Use of the Hough transformation to detect lines and curves in pictures, Commun. ACM, vol.15, issue.1, pp.11-15, 1972.

S. Durand, J. Fadili, and M. Nikolova, Multiplicative noise removal using l1 fidelity on frame coefficients, Journal of Mathematical Imaging and Vision, vol.36, issue.3, pp.201-226, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00345119

M. Elad and M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries, IEEE Transactions on Image processing, vol.15, issue.12, pp.3736-3745, 2006.

M. Elad, P. Milanfar, and R. Rubinstein, Analysis versus synthesis in signal priors, Inverse Problems, vol.23, issue.3, p.947, 2007.

C. Yonina, P. Eldar, H. Kuppinger, and . Bolcskei, Block-sparse signals: Uncertainty relations and efficient recovery, IEEE Transactions on Signal Processing, vol.58, issue.6, pp.3042-3054, 2010.

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth et al., Sparsity based denoising of spectral domain optical coherence tomography images, Biomedical Optics Express, vol.3, issue.5, pp.927-942, 2012.

L. Fang, S. Li, P. Ryan, Q. Mcnabb, . Nie et al., Fast acquisition and reconstruction of optical coherence tomography images via sparse representation, IEEE Transactions on Medical Imaging, vol.32, issue.11, pp.2034-2049, 2013.

L. Fang, S. Li, D. Cunefare, and S. Farsiu, Segmentation based sparse reconstruction of optical coherence tomography images, IEEE Transactions on Medical Imaging, vol.36, issue.2, pp.407-421, 2017.

L. Feng, R. Grimm, K. T. Block, H. Chandarana, S. Kim et al., Golden-angle radial sparse parallel mri: Combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric mri, Magnetic Resonance in Medicine, vol.72, issue.3, pp.707-717, 2014.

. Adolph-f-fercher, K. Ch, G. Hitzenberger, . Kamp, Y. Sy et al., Measurement of intraocular distances by backscattering spectral interferometry, Optics Communications, vol.117, issue.1-2, pp.43-48, 1995.

A. T. Mário, R. D. Figueiredo, S. Nowak, and . Wright, Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE Journal of Selected Topics in Signal Processing, vol.1, issue.4, pp.586-597, 2007.

M. A. Figueiredo and J. M. Bioucas-dias, Restoration of Poissonian images using alternating direction optimization, IEEE Transactions on Image Processing, vol.19, issue.12, pp.3133-3145, 2010.

M. Fornasier and H. Rauhut, Recovery algorithms for vectorvalued data with joint sparsity constraints, SIAM Journal on Numerical Analysis, vol.46, issue.2, pp.577-613, 2008.

S. Foucart and H. Rauhut, A mathematical introduction to compressive sensing, Birkhäuser Basel, vol.1, 2013.

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Transactions on Pattern Analysis and Machine Intelligence, issue.2, pp.157-166, 1982.

J. Fujimoto and E. Swanson, The development, commercialization, and impact of optical coherence tomography. Investigative Ophthalmology & visual science, vol.57, pp.1-13, 2016.

D. Gabor, Theory of communication. part 1: The analysis of information, Journal of the Institution of Electrical Engineers-Part III: Radio and Communication Engineering, vol.93, issue.26, pp.429-441, 1946.

Y. Gan and Q. Xu, An improved sos method for generating multiple uncorrelated rayleigh fading waveforms, IEEE Communications Letters, vol.14, issue.7, pp.641-643, 2010.

Y. Gan, D. Tsay, B. Syed, C. C. Amir, C. P. Marboe et al., Automated classification of optical coherence tomography images of human atrial tissue, Journal of Biomedical Optics, vol.21, issue.10, p.101407, 2016.

Y. Gan, Image analytic tools for tissue characterization using optical coherence tomography, 2017.

M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, Denoising and 4D visualization of OCT images, Optics Express, vol.16, issue.16, pp.12313-12333, 2008.

M. K. Garvin, M. D. Abramoff, X. Wu, S. R. Russell, T. L. Burns et al., Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images, IEEE Transactions on Medical Imaging, vol.28, pp.1436-1447, 2009.

S. Geman and D. Geman, Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, issue.6, pp.721-741, 1984.

C. Anna, S. Gilbert, P. Guha, . Indyk, M. Muthukrishnan et al., Near-optimal sparse fourier representations via sampling, Proceedings of the thiry-fourth annual ACM Symposium on Theory of Computing, pp.152-161, 2002.

S. Gleichman and Y. C. Eldar, Blind compressed sensing, IEEE Transactions on Information Theory, vol.57, issue.10, pp.6958-6975, 2011.

B. Antoine-g-godin, L. Lounis, and . Cognet, Superresolution microscopy approaches for live cell imaging, Biophysical Journal, vol.107, issue.8, pp.1777-1784, 2014.

. Joseph-w-goodman, Introduction to Fourier optics, 2005.

M. Gora, K. Karnowski, M. Szkulmowski, J. Bartlomiej, R. Kaluzny et al., Ultra high-speed swept source oct imaging of the anterior segment of human eye at 200 khz with adjustable imaging range, Optics Express, vol.17, issue.17, pp.14880-14894, 2009.

R. Gribonval and M. Nielsen, Sparse representations in unions of bases, IEEE Transactions on Information Theory, vol.49, issue.12, pp.3320-3325, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00071943

K. Grünewald, O. Medalia, A. Gross, A. C. Steven, and W. Baumeister, Prospects of electron cryotomography to visualize macromolecular complexes inside cellular compartments: implications of crowding, Biophysical Chemistry, vol.100, issue.1-3, pp.577-591, 2002.

W. Guo and W. Yin, Edgecs: Edge guided compressive sensing reconstruction, 77440L-77440L. International Society for Optics and Photonics, 2010.

M. G. Gustafsson, Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy, Journal of Microscopy, vol.198, issue.2, pp.82-87, 2000.

G. L. Mats, L. Gustafsson, . Shao, M. Peter, . Carlton et al., Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination, Biophysical Journal, vol.94, issue.12, pp.4957-4970, 2008.

N. Gustafsson, S. Culley, G. Ashdown, D. M. Owen, P. M. Pereira et al., Fast live-cell conventional fluorophore nanoscopy with imagej through super-resolution radial fluctuations, Nature Communications, vol.7, p.12471, 2016.

. Sa-haider, . Cameron, . Siva, . Lui, . Mj-shafiee et al., Fluorescence microscopy image noise reduction using a stochastically-connected random field model, Scientific Reports, vol.6, p.20640, 2016.

P. Lida, M. Hariri, . Mino-kenudson, B. Matthew, E. J. Applegate et al., Toward the guidance of transbronchial biopsy: identifying pulmonary nodules with optical coherence tomography, Chest, vol.144, issue.4, pp.1261-1268, 2013.

K. Hayashi, M. Nagahara, and T. Tanaka, A user's guide to compressed sensing for communications systems, IEICE Transactions on Communications, vol.96, issue.3, pp.685-712, 2013.

J. A. Michael-r-hee, E. A. Izatt, D. Swanson, J. S. Huang, . Schuman et al., Optical coherence tomography of the human retina, Archives of Ophthalmology, vol.113, issue.3, pp.325-332, 1995.

R. Heintzmann and T. Huser, Super-resolution structured illumination microscopy, Chemical Reviews, vol.117, issue.23, pp.13890-13908, 2017.

W. Heisenberg, The physical principles of the quantum theory, Courier Corporation, 1949.

W. Stefan, J. Hell, and . Wichmann, Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy, Optics Letters, vol.19, issue.11, pp.780-782, 1994.

A. Matthew, T. Herman, and . Strohmer, High-resolution radar via compressed sensing, IEEE Transactions on Signal Processing, vol.57, issue.6, pp.2275-2284, 2009.

M. Hirata, A. Tsujikawa, A. Matsumoto, M. Hangai, S. Ooto et al., Macular choroidal thickness and volume in normal subjects measured by swept-source optical coherence tomography, Investigative Ophthalmology & Visual Science, vol.52, issue.8, pp.4971-4978, 2011.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, G. William et al., Optical coherence tomography, Science, vol.254, issue.5035, pp.1178-1181, 1991.
URL : https://hal.archives-ouvertes.fr/tel-01957283

B. Huang, M. Bates, and X. Zhuang, Super-resolution fluorescence microscopy, Annual Review of Biochemistry, vol.78, pp.993-1016, 2009.

Y. Huang, M. K. Ng, and Y. Wen, A new total variation method for multiplicative noise removal, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.20-40, 2009.

J. Huang and T. Zhang, The benefit of group sparsity, The Annals of Statistics, vol.38, issue.4, pp.1978-2004, 2010.

J. Huang and F. Yang, Compressed magnetic resonance imaging based on wavelet sparsity and nonlocal total variation, 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.968-971, 2012.

A. Jezierska, C. Chaux, J. Pesquet, and H. Talbot, An EM approach for Poisson-Gaussian noise modeling, IEEE European Signal Processing Conference (EUSIPCO), pp.2244-2248, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00733633

A. Jezierska, C. Chaux, J. Pesquet, H. Talbot, and G. Engler, An em approach for time-variant poissongaussian model parameter estimation, IEEE Transactions on Signal Processing, vol.62, issue.1, pp.17-30, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00766686

A. Jost and R. Heintzmann, Superresolution multidimensional imaging with structured illumination microscopy, Annual Review of Materials Research, vol.43, pp.261-282, 2013.

S. Karanam, Y. Li, and R. J. Radke, Sparse re-id: Block sparsity for person re-identification, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.33-40, 2015.

M. Steven and . Kay, Fundamentals of statistical signal processing, volume i: Estimation theory (v. 1). PTR Prentice-Hall, 1993.

C. Kervrann, PEWA: Patch-based exponentially weighted aggregation for image denoising, Advances in Neural Information Processing Systems, pp.2150-2158, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01103358

M. Keuper, T. Schmidt, M. Temerinac-ott, J. Padeken, P. Heun et al., Blind deconvolution of widefield fluorescence microscopic data by regularization of the optical transfer function (otf), Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2179-2186, 2013.

Y. Kim, S. S. Narayanan, and K. S. Nayak, Accelerated threedimensional upper airway MRI using compressed sensing, Magnetic Resonance in Medicine, vol.61, issue.6, pp.1434-1440, 2009.

A. Thomas, S. W. Klar, and . Hell, Subdiffraction resolution in far-field fluorescence microscopy, Optics Letters, vol.24, issue.14, pp.954-956, 1999.

T. Klein, W. Wieser, C. M. Eigenwillig, R. Benjamin-r-biedermann, and . Huber, Megahertz oct for ultrawide-field retinal imaging with a 1050nm fourier domain mode-locked laser, Optics Express, vol.19, issue.4, pp.3044-3062, 2011.

P. Kner, E. R. Bryant-b-chhun, L. Griffis, . Winoto, . Mats et al., Super-resolution video microscopy of live cells by structured illumination, Nature Methods, vol.6, issue.5, p.339, 2009.

M. Kowalski, Sparse regression using mixed norms, Applied and Computational Harmonic Analysis, vol.27, issue.3, pp.303-324, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00202904

F. Krahmer and H. Rauhut, Structured random measurements in signal processing, GAMM-Mitteilungen, vol.37, issue.2, pp.217-238, 2014.

B. Joseph and . Kruskal, On the shortest spanning subtree of a graph and the traveling salesman problem, Proceedings of the American Mathematical Society, vol.7, issue.1, pp.48-50, 1956.

D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, Adaptive restoration of images with speckle, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.35, issue.3, pp.373-383, 1987.

A. Irina, N. D. Kuznetsova, . Gladkova, M. Valentin, J. L. Gelikonov et al., Oct in gynecology. Optical Coherence Tomography: Technology and Applications, pp.2305-2334, 2015.

P. C. Lauterbur, Image formation by induced local interactions: examples employing nuclear magnetic resonance, Nature, vol.242, pp.190-191, 1973.

Y. Le-montagner, E. D. Angelini, and J. Olivo-marin, An unbiased risk estimator for image denoising in the presence of mixed poisson-gaussian noise, IEEE Transactions on Image Processing, vol.23, issue.3, pp.1255-1268, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02286836

T. Le, R. Chartrand, and T. J. Asaki, A variational approach to reconstructing images corrupted by poisson noise, Journal of Mathematical Imaging and Vision, vol.27, issue.3, pp.257-263, 2007.

R. Leary, Z. Saghi, A. Paul, D. Midgley, and . Holland, Compressed sensing electron tomography, Ultramicroscopy, vol.131, pp.70-91, 2013.

E. Lebed, J. Paul, . Mackenzie, V. Marinko, M. Sarunic et al., Rapid volumetric oct image acquisition using compressive sampling, Optics Express, vol.18, issue.20, pp.21003-21012, 2010.

M. Lebrun, An analysis and implementation of the bm3d image denoising method, Image Processing On Line, vol.2, pp.175-213, 2012.

J. Lee, Digital image enhancement and noise filtering by use of local statistics, IEEE Transactions on Pattern Analysis and Machine Intelligence, issue.2, pp.165-168, 1980.

H. Lee, A. Battle, R. Raina, and A. Y. Ng, Efficient sparse coding algorithms, Advances in Neural Information Processing Systems, vol.19, p.801, 2007.

Y. Lemontagner, Algorithmic solutions toward applications of compressed sensing for optical imaging, 2013.

C. Li, An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing, 2010.

D. Li, L. Shao, B. Chen, X. Zhang, M. Zhang et al., Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics, Science, vol.349, issue.6251, p.3500, 2015.

Y. Ling, Y. Gan, X. Yao, and C. P. Hendon, Phase-noise analysis of swept-source optical coherence tomography systems, Optics Letters, vol.42, issue.7, pp.1333-1336, 2017.

Y. Ling, High-speed phase-stable swept source optical coherence tomography: functional imaging and biomedical applications, 2018.

G. Sajan, M. Lingala, and . Jacob, Blind compressive sensing dynamic mri, IEEE Transactions on Medical Imaging, vol.32, issue.6, pp.1132-1145, 2013.

X. Liu and J. U. Kang, Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography, vol.18, pp.22010-22019, 2010.

C. P. Loizou, C. S. Pattichis, C. I. Christodoulou, R. S. Istepanian, M. Pantziaris et al., Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol.52, issue.10, pp.1653-1669, 2005.

C. Louchet and L. Moisan, Total variation denoising using posterior expectation, IEEE European Signal Processing Conference (EU-SIPCO), pp.1-5, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00258849

C. Louchet and L. Moisan, Total variation denoising using iterated conditional expectation, IEEE European Signal Processing Conference (EUSIPCO), pp.1592-1596, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01214735

F. Luisier, C. Vonesch, T. Blu, and M. Unser, Fast interscale wavelet denoising of poisson-corrupted images, Signal Processing, vol.90, issue.2, pp.415-427, 2010.

M. Lustig, D. Donoho, and J. Pauly, Sparse mri: The application of compressed sensing for rapid mr imaging, Magnetic Resonance in Medicine, vol.58, issue.6, pp.1182-1195, 2007.

L. Ma, L. Moisan, J. Yu, and T. Zeng, A dictionary learning approach for poisson image deblurring, IEEE Transactions on Medical Imaging, vol.32, issue.7, pp.1277-1289, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00834517

M. Makitalo and A. Foi, Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise, IEEE Transactions on Image Processing, vol.22, issue.1, pp.91-103, 2013.

M. Mäkitalo and A. Foi, Noise parameter mismatch in variance stabilization, with an application to poisson-gaussian noise estimation, IEEE Transactions on Image Processing, vol.23, issue.12, pp.5348-5359, 2014.

S. Mallat and G. Peyré, A review of bandlet methods for geometrical image representation, Numerical Algorithms, vol.44, issue.3, pp.205-234, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00359744

A. Mariampillai, A. Beau, E. H. Standish, M. Moriyama, N. R. Khurana et al., Speckle variance detection of microvasculature using swept-source optical coherence tomography, Optics Letters, vol.33, issue.13, pp.1530-1532, 2008.

M. Marim, E. Angelini, and J. Olivo-marin, Denoising in fluorescence microscopy using compressed sensing with multiple reconstructions and non-local merging, International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp.3394-3397, 2010.

M. Marim, A compressed Sensing framework for biological microscopy, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00586625

M. Marim, E. Angelini, J. Olivo-marin, and M. Atlan, Off-axis compressed holographic microscopy in lowlight conditions, Optics Letters, vol.36, issue.1, pp.79-81, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00553203

L. Martínez-león, P. Clemente, Y. Mori, V. Climent, J. Lancis et al., Single-pixel digital holography with phase-encoded illumination, Optics Express, vol.25, issue.5, pp.4975-4984, 2017.

T. Marvdashti, L. Duan, Z. Sumaira, . Aasi, Y. Jean et al., Classification of basal cell carcinoma in human skin using machine learning and quantitative features captured by polarization sensitive optical coherence tomography, Biomedical Optics Express, vol.7, issue.9, pp.3721-3735, 2016.

M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin et al., Wavelet denoising of multiframe optical coherence tomography data, Optics Express, vol.3, issue.3, pp.572-589, 2012.

E. Mcleod and A. Ozcan, Unconventional methods of imaging: computational microscopy and compact implementations, Reports on Progress in Physics, vol.79, issue.7, p.76001, 2016.

M. Mishali and Y. C. Eldar, Blind multi-band signal reconstruction: Compressed sensing for analog signals, 2007.

M. Mishali and Y. C. Eldar, From theory to practice: Subnyquist sampling of sparse wideband analog signals, IEEE Journal of Selected Topics in Signal Processing, vol.4, issue.2, pp.375-391, 2010.

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, Intra-retinal layer segmentation in optical coherence tomography images, Optics Express, vol.26, issue.17, pp.23719-23728, 2009.

N. Mohan, I. Stojanovic, C. Karl, E. A. Bahaa, . Saleh et al., Compressed sensing in optical coherence tomography, Proc. SPIE, vol.7570, p.75700, 2010.

E. Mudry, K. Belkebir, J. Girard, L. Savatier, C. Moal et al., Structured illumination microscopy using unknown speckle patterns, Nature Photonics, vol.6, issue.5, pp.312-315, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00738270

O. Myakinin, V. Zakharov, I. Bratchenko, D. Kornilin, and A. Khramov, A digital method for lossless and lossy compression of high definition optical coherence tomography data, Biomedial Optics, 2014.

. Na-nassif, . Cense, . Park, . Mc-pierce, . Sh-yun et al., In vivo high-resolution videorate spectral-domain optical coherence tomography of the human retina and optic nerve, Optics Express, vol.12, issue.3, pp.367-376, 2004.

. Balas-kausik-natarajan, Sparse approximate solutions to linear systems, SIAM Journal on Computing, vol.24, issue.2, pp.227-234, 1995.

Y. Nesterov, Smooth minimization of non-smooth functions, Mathematical Programming, vol.103, issue.1, pp.127-152, 2005.

Y. Nesterov, Gradient methods for minimizing composite objective function, 2007.

J. Olivo-marin, Extraction of spots in biological images using multiscale products, Pattern Recognition, vol.35, issue.9, pp.1989-1996, 2002.

F. Orieux, E. Sepulveda, V. Loriette, B. Dubertret, and J. Olivo-marin, Bayesian estimation for optimized structured illumination microscopy, Transactions on Image Processing, vol.21, pp.601-614, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01225862

Y. Chandra-pati, R. Rezaiifar, and P. Krishnaprasad, Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition, Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, pp.40-44, 1993.

N. Pavillon and . Smith, Compressed sensing laser scanning microscopy, Optics Express, vol.24, issue.26, pp.30038-30052, 2016.

P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.7, pp.629-639, 1990.

S. Vladimir, . Petrovic, S. Costas, and . Xydeas, Gradient-based multiresolution image fusion, IEEE Transactions on Image Processing, vol.13, issue.2, pp.228-237, 2004.

E. Götz, H. Pfander, J. A. Rauhut, and . Tropp, The restricted isometry property for time-frequency structured random matrices. Probability Theory and Related Fields, vol.156, pp.707-737, 2013.

C. Poon, On the role of total variation in compressed sensing, Journal on Imaging Sciences, vol.8, issue.1, pp.682-720, 2015.

H. Rauhut, Stability results for random sampling of sparse trigonometric polynomials, IEEE Transactions on Information theory, vol.54, issue.12, pp.5661-5670, 2008.

Y. Rivenson, A. Stern, and B. Javidi, Overview of compressive sensing techniques applied in holography, Applied Optics, vol.52, issue.1, pp.423-432, 2013.

H. Percy-robertson, The uncertainty principle, Physical Review, vol.34, issue.1, p.163, 1929.

P. Rodríguez, E. Sepulveda, B. Dubertret, and V. Loriette, Axial coding in full-field microscopy using three-dimensional structured illumination implemented with no moving parts, Optics Letters, vol.33, issue.14, pp.1617-1619, 2008.

C. Rose and M. D. Smith, Mathematical statistics with mathematica, chapter 7.2c: k-statistics: Unbiased estimators of cumulants, 2002.

R. Rubinstein, M. Zibulevsky, and M. Elad, Efficient implementation of the k-svd algorithm using batch orthogonal matching pursuit, Cs Technion, vol.40, issue.8, pp.1-15, 2008.

R. Rubinstein, T. Peleg, and M. Elad, Analysis k-svd: A dictionarylearning algorithm for the analysis sparse model, IEEE Transactions on Signal Processing, vol.61, issue.3, pp.661-677, 2013.

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1, pp.259-268, 1992.

L. Rudin, P. Lions, and S. Osher, Multiplicative denoising and deblurring: theory and algorithms, Geometric Level Set Methods in Imaging, Vision, and Graphics, pp.103-119, 2003.

E. Ruska and M. Knoll, Die magnetische sammelspule für schnelle elektronenstrahlen. (The Magnetic Concentrating Coil for Fast Electron Beams, Z. techn. Physik, vol.12, pp.389-400, 1931.

J. Michael, M. Rust, X. Bates, and . Zhuang, Sub-diffractionlimit imaging by stochastic optical reconstruction microscopy (storm), Nature Methods, vol.3, issue.10, p.793, 2006.

P. Ruusuvuori, A. Lehmussola, J. Selinummi, T. Rajala, H. Huttunen et al., Benchmark set of synthetic images for validating cell image analysis algorithms, IEEE European Signal Processing Conference (EUSIPCO), pp.1-5, 2008.

Q. Sabatier, . Sio-hoi, R. Ieng, and . Benosman, Asynchronous event-based fourier analysis, IEEE Transactions on Image Processing, vol.26, issue.5, pp.2192-2202, 2017.

J. Steffen, S. W. Sahl, S. Hell, and . Jakobs, Fluorescence nanoscopy in cell biology, Nature Reviews Molecular Cell Biology, vol.18, issue.11, p.685, 2017.

J. Salmon and Y. Strozecki, Patch reprojections for non-local methods, Signal Processing, vol.92, issue.2, pp.477-489, 2012.

J. Salmon, Z. Harmany, C. Deledalle, and R. Willett, Poisson noise reduction with non-local PCA, Journal of Mathematical Imaging and Vision, vol.48, issue.2, pp.279-294, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00957837

P. Sarder and A. Nehorai, Deconvolution methods for 3-d fluorescence microscopy images, IEEE Signal Processing Magazine, vol.23, issue.3, pp.32-45, 2006.

A. Sawatzky, C. Brune, J. Müller, and M. Burger, Total variation processing of images with poisson statistics, Computer Analysis of Images and Patterns (CAIP), pp.533-540, 2009.

M. Joseph and . Schmitt, Optical coherence tomography (oct): a review, IEEE Journal of Selected Topics in Quantum Electronics, vol.5, issue.4, pp.1205-1215, 1999.

S. Schwartz, A. Wong, and D. A. Clausi, Compressive fluorescence microscopy using saliency-guided sparse reconstruction ensemble fusion, Optics Express, vol.20, issue.16, pp.17281-17296, 2012.

S. Setzer, G. Steidl, and T. Teuber, Deblurring Poissonian images by split Bregman techniques, Journal of Visual Communication and Image Representation, vol.21, issue.3, pp.193-199, 2010.

Z. Shangguan, Y. Shen, P. Li, and Z. Ding, Wavenumber calibration and phase measurement in swept source optical coherence tomography (in Chinese), Acta Physica Sinica, vol.65, issue.3, p.34201, 2016.

N. Simon, J. Friedman, T. Hastie, and R. Tibshirani, A sparse-group lasso, Journal of Computational and Graphical Statistics, vol.22, issue.2, pp.231-245, 2013.

P. Pratul, L. A. Srinivasan, . Kim, S. Priyatham, . Mettu et al., Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images, Biomedical Optics Express, vol.5, issue.10, pp.3568-3577, 2014.

J. Starck, M. Donoho, and . Elad, Redundant multiscale transforms and their application for morphological component separation, p.52061, 2004.

J. Starck, Y. Moudden, and J. Bobin, Polarized wavelets and curvelets on the sphere, Astronomy & Astrophysics, vol.497, issue.3, pp.931-943, 2009.

C. M. Stein, Estimation of the mean of a multivariate normal distribution. The Annals of Statistics, pp.1135-1151, 1981.

V. Studer, J. Bobin, and M. Chahid, Compressive fluorescence microscopy for biological and hyperspectral imaging. Proceedings of the National Academy of, Sciences, vol.109, issue.26, pp.1679-1687, 2012.

J. Guillermo, E. Tearney, T. Regar, T. Akasaka, P. Adriaenssens et al., Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the international working group for intravascular optical coherence tomography standardization and validation, Journal of the American College of Cardiology, vol.59, issue.12, pp.1058-1072, 2012.

G. Teschke and R. Ramlau, An iterative algorithm for nonlinear inverse problems with joint sparsity constraints in vector-valued regimes and an application to color image inpainting, Inverse Problems, vol.23, issue.5, p.1851, 2007.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

M. Andreas, M. E. Tillmann, and . Pfetsch, The computational complexity of the restricted isometry property, the nullspace property, and related concepts in compressed sensing, IEEE Transactions on Information Theory, vol.60, issue.2, pp.1248-1259, 2014.

A. Joel, A. C. Tropp, and . Gilbert, Signal recovery from random measurements via orthogonal matching pursuit, IEEE Transactions on Information Theory, vol.53, issue.12, pp.4655-4666, 2007.

E. Van-den, 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.

E. Van-den, M. P. Berg, and . Friedlander, Sparse optimization with least-squares constraints, SIAM Journal on Optimization, vol.21, issue.4, pp.1201-1229, 2011.

J. Robert and . Vanderbei, Fast fourier optimization, Mathematical Programming Computation, vol.4, issue.1, pp.53-69, 2012.

A. Veeraraghavan, D. Reddy, and R. Raskar, Coded strobing photography: Compressive sensing of high speed periodic videos, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.4, pp.671-686, 2011.

P. Vermeulen, F. Zhan, J. Orieux, Z. Olivo-marin, V. Lenkei et al., Out-of-focus background subtraction for fast structured illumination super-resolution microscopy of optically thick samples, Journal of Microscopy, vol.259, issue.3, pp.257-268, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01225859

M. Wakin, S. Becker, E. Nakamura, M. Grant, E. Sovero et al., Azita Emami-Neyestanak, and Emmanuel Candes. A nonuniform sampler for wideband spectrally-sparse environments, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol.2, issue.3, pp.516-529, 2012.

K. Ruikang, Z. Wang, and . Ma, A practical approach to eliminate autocorrelation artefacts for volume-rate spectral domain optical coherence tomography, Physics in Medicine & Biology, vol.51, issue.12, p.3231, 2006.

K. Ruikang, L. Wang, and . An, Doppler optical micro-angiography for volumetric imaging of vascular perfusion in vivo, Optics Express, vol.17, issue.11, pp.8926-8940, 2009.

Z. Wang and A. C. Bovik, Mean squared error: Love it or leave it? a new look at signal fidelity measures, IEEE Signal Processing Magazine, vol.26, issue.1, pp.98-117, 2009.

Z. Wang and G. R. Arce, Variable density compressed image sampling, IEEE Transactions on Image Processing, vol.19, issue.1, pp.264-270, 2010.

S. Wang, S. David, M. D. Lakomy, A. L. Garcia, . Lopez et al., Four-dimensional live imaging of hemodynamics in mammalian embryonic heart with doppler optical coherence tomography, Journal of Biophotonics, vol.9, issue.8, pp.837-847, 2016.

Z. Wang, L. Spinoulas, K. He, L. Tian, O. Cossairt et al., Compressive holographic video, Optics Express, vol.25, issue.1, pp.250-262, 2017.

J. Weickert, M. A. Bm-ter-haar-romeny, and . Viergever, Efficient and reliable schemes for nonlinear diffusion filtering, IEEE Transactions on Image Processing, vol.7, issue.3, pp.398-410, 1998.

P. Weiss, Algorithmes rapides d'optimisation convexe. Applications à la reconstruction d'images et à la détection de changements, 2008.
URL : https://hal.archives-ouvertes.fr/tel-00349452

J. Whitaker, H. Raju, C. Taylor, and C. Rinaldi, Accelerated idioventricular rhythm after left atrial tachycardia ablation as a marker of acute coronary ischemia, HeartRhythm Case Reports, vol.1, issue.3, pp.99-102, 2015.

K. Wicker, Non-iterative determination of pattern phase in structured illumination microscopy using auto-correlations in fourier space, Optics Express, vol.21, issue.21, pp.24692-24701, 2013.

W. Wieser, R. Benjamin, T. Biedermann, C. M. Klein, R. Eigenwillig et al., Multi-megahertz oct: High quality 3d imaging at 20 million a-scans and 4.5 gvoxels per second, Optics Express, vol.18, issue.14, pp.14685-14704, 2010.

R. Willett, Multiscale analysis of photon-limited astronomical images, Statistical Challenges in Modern Astronomy IV, vol.371, p.247, 2007.

S. O. Katrin-i-willig, V. Rizzoli, R. Westphal, S. W. Jahn, and . Hell, Sted microscopy reveals that synaptotagmin remains clustered after synaptic vesicle exocytosis, Nature, vol.440, issue.7086, p.935, 2006.

M. Woringer, X. Darzacq, C. Zimmer, and M. Mir, Faster and less phototoxic 3d fluorescence microscopy using a versatile compressed sensing scheme, Optics Express, vol.25, issue.12, pp.13668-13683, 2017.
URL : https://hal.archives-ouvertes.fr/pasteur-02074973

J. Stephen, R. D. Wright, . Nowak, and . Figueiredo, Sparse reconstruction by separable approximation, IEEE Transactions on Signal Processing, vol.57, issue.7, pp.2479-2493, 2009.

Y. Wu, P. Ye, O. Iftekhar, G. R. Mirza, D. W. Arce et al., Experimental demonstration of an optical-sectioning compressive sensing microscope (csm), Optics Express, vol.18, issue.24, pp.24565-24578, 2010.

C. A. Yang, Z. Reisman, Y. Wang, M. Fukuma, N. Hangai et al., Automated layer segmentation of macular OCT images using dual-scale gradient information, Optics Express, vol.20, issue.18, pp.21293-21307, 2010.

X. Yao, Y. Gan, C. Charles, C. P. Marboe, and . Hendon, Myocardial imaging using ultrahigh-resolution spectral domain optical coherence tomography, Journal of Biomedical Optics, vol.21, issue.6, pp.61006-061006, 2016.

Y. Yasuno, V. D. Madjarova, S. Makita, M. Akiba, A. Morosawa et al., Three-dimensional and high-speed swept-source optical coherence tomography for in vivo investigation of human anterior eye segments, Optics Express, vol.13, issue.26, pp.10652-10664, 2005.

Y. Yasuno, Y. Hong, S. Makita, M. Yamanari, M. Akiba et al., In vivo highcontrast imaging of deep posterior eye by 1-µm swept source optical coherence tomography and scattering optical coherence angiography, Optics Express, vol.15, issue.10, pp.6121-6139, 2007.

S. Yennek, M. Burute, M. Théry, and S. Tajbakhsh, Cell adhesion geometry regulates non-random dna segregation and asymmetric cell fates in mouse skeletal muscle stem cells, Cell Reports, vol.7, issue.4, pp.961-970, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01141085

W. Yin, S. Osher, D. Goldfarb, and J. Darbon, Bregman iterative algorithms for \ell_1-minimization with applications to compressed sensing, SIAM Journal on Imaging Sciences, vol.1, issue.1, pp.143-168, 2008.

P. Yin, Y. Lou, Q. He, and J. Xin, Minimization of 1?2 for compressed sensing, SIAM Journal on Scientific Computing, vol.37, issue.1, pp.536-563, 2015.

M. Young, E. Lebed, Y. Jian, J. Paul, . Mackenzie et al., Real-time high-speed volumetric imaging using compressive sampling optical coherence tomography, Biomedical Optics Express, vol.2, issue.9, pp.2690-2697, 2011.

Y. Yu and . Scott-t-acton, Speckle reducing anisotropic diffusion, IEEE Transactions on Image Processing, vol.11, issue.11, pp.1260-1270, 2002.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, issue.1, pp.49-67, 2006.

. Sh-yun, . Tearney, . Bouma, J. Park, and . Boer, High-speed spectral-domain optical coherence tomography at 1.3 µm wavelength, Optics Express, vol.11, issue.26, pp.3598-3604, 2003.

B. Zhang, M. J. Fadili, J. Starck, and J. Olivo-marin, Multiscale variance-stabilizing transform for mixed-Poisson-Gaussian processes and its applications in bioimaging, IEEE International Conference on Image Processing, vol.6, p.233, 2007.

B. Zhang, Contributions à la microscopie à fluorescence en imagerie biologique: modélisation de la PSF, restauration d'images et détection super-résolutive, 2007.

S. Zhang, J. Huang, Y. Huang, Y. Yu, H. Li et al., Automatic image annotation using group sparsity, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3312-3319, 2010.

Y. Zhang, Theory of compressive sensing via 1 -minimization: a nonrip analysis and extensions, Journal of the Operations Research Society of China, vol.1, issue.1, pp.79-105, 2013.

Y. Zhao, Q. Chen, X. Sui, and H. Gao, Super resolution imaging based on a dynamic single pixel camera, IEEE Photonics Journal, vol.9, issue.2, pp.1-11, 2017.

L. Zhu, W. Zhang, D. Elnatan, and B. Huang, Faster storm using compressed sensing, Nature Methods, vol.9, issue.7, p.721, 2012.