E. H. Adelson and J. Y. Wang, Single lens stereo with a plenoptic camera, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, 1992.
DOI : 10.1109/34.121783

R. J. Adrian, Twenty years of particle image velocimetry, Experiments in Fluids, vol.10, issue.2, p.39, 2005.
DOI : 10.1007/s00348-005-0991-7

R. J. Adrian, Bibliography of Particle Velocimetry Using Imaging Methods: 1917-1995, DLR, Göttingen Anniversary Edition, 2009.

R. J. Adrian and J. Westerweel, Particle Image Velocimery. Cambridge Aerospace Series, 2010.

R. J. Adrian and C. Yao, Pulsed laser technique application to liquid and gaseous flows and the scattering power of seed materials, Applied Optics, vol.24, issue.1, 1985.
DOI : 10.1364/AO.24.000044

H. Albrecht, M. Borys, N. Damaschke, and C. Tropea, Laser Doppler and Phase Doppler Measurement Techniques, 2002.
DOI : 10.1007/978-3-662-05165-8

M. V. Alfonso, J. M. Bioucas-dias, and M. A. Figueiredo, An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems

A. H. Andersen and A. C. Kak, Simultaneous Algebraic Reconstruction Technique (SART): a superior implementation of the ART algorithm, Ultasonic Imaging, vol.6, 1984.

K. P. Angele and B. Muhammad-klingmann, A simple model for the effect of peak-locking on the accuracy of boundary layer turbulence statistics in digital PIV, Experiments in Fluids, vol.23, issue.9
DOI : 10.1007/s00348-004-0908-x

C. C. Antonio-cenedese, F. Furia, L. Marchetti, M. Moroni, and L. Shindler, 3D particle reconstruction using light field imaging, 2012.

E. Arnaud, É. Mémin, R. Sosa, and G. Artana, A Fluid Motion Estimator for Schlieren Image Velocimetry, ECCV, 2006.
DOI : 10.1007/11744023_16
URL : https://hal.archives-ouvertes.fr/inria-00590206

M. P. Arroyo and C. A. Greated, Stereoscopic particle image velocimetry, Measurement Science and Technology, vol.2, issue.12, 1991.
DOI : 10.1088/0957-0233/2/12/012

M. P. Arroyo and K. D. Hinsch, Recent Developments of??PIV towards??3D??Measurements, Particle Image Velocimery : New Developments and Recent Applications, 2008.
DOI : 10.1007/978-3-540-73528-1_7

C. H. Atkinson, Reconstruction techniques for tomographic PIV (tomo-PIV) of turbulent boundary layer, SALTFM, 2008.

C. H. Atkinson and J. Soria, An efficient simultaneous reconstruction technique for tomographic particle image velocimetry, Experiments in Fluids, vol.45, issue.4, 2009.
DOI : 10.1007/s00348-009-0728-0

D. Auroux and J. Fehrenbach, Identification of velocity fields for geophysical fluids from a sequence of images, Experiments in Fluids, vol.28, issue.1, 2011.
DOI : 10.1007/s00348-010-0926-9
URL : https://hal.archives-ouvertes.fr/hal-00905015

I. Barbu, C. Herzet, and E. Mémin, Sparse models and pursuit algorithms for PIV tomography, Forum on recent developments in Volume Reconstruction techniques applied to 3D fluid and solid mechanics, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00683436

I. Barbu, C. Herzet, and E. Mémin, Sparse models and pursuit algorithms for PIV tomography, FVR, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00683436

I. Barbu, C. Herzet, and É. Mémin, Joint Estimation of Volume and Velocity in TomoPIV, PIV, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00880712

A. Bartoli and U. Castellani, 3D Shape Registration, 3D Imaging, Analysis and Applications, 2012.

J. Barzilai and J. M. Borwein, Two-Point Step Size Gradient Methods, IMA Journal of Numerical Analysis, vol.8, issue.1, 1988.
DOI : 10.1093/imanum/8.1.141

S. S. Beauchemin and J. L. Barron, The computation of optical flow, ACM Computing Surveys, vol.27, issue.3, 1995.
DOI : 10.1145/212094.212141

A. Beck and M. Teboulle, Mirror descent and nonlinear projected subgradient methods for convex optimization, Operations Research Letters, vol.31, issue.3, 2003.
DOI : 10.1016/S0167-6377(02)00231-6

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, 2009.
DOI : 10.1137/080716542

F. Becker, B. Wieneke, S. Petra, A. Schröder, and C. Schnörr, Variational Adaptive Correlation Method for Flow Estimation, IEEE Transactions on Image Processing, vol.21, issue.6, p.2012
DOI : 10.1109/TIP.2011.2181524

J. Belden, T. T. Truscott, M. C. Axiak, and A. H. Techet, Three-dimensional synthetic aperture particle image velocimetry, Measurement Science and Technology, vol.21, issue.12, 2010.
DOI : 10.1088/0957-0233/21/12/125403

P. J. Besl and N. D. Mckay, A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, 1992.
DOI : 10.1109/34.121791

A. V. Bilsky, V. A. Lozhkin, D. M. Markovich, M. P. Tokarev, E. G. Birgin et al., Low computation cost reconstruction technique for Tomo-PIV Nonmonotone spectral projected gradient methods on convex sets, FVR, 2000.

M. J. Black, The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields, Computer Vision and Image Understanding, vol.63, issue.1, 1996.
DOI : 10.1006/cviu.1996.0006

T. Blumensath and . M. Davies, Iterative hard thresholding for compressed sensing, Applied and Computational Harmonic Analysis, vol.27, issue.3
DOI : 10.1016/j.acha.2009.04.002

C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles, 1983.
DOI : 10.1002/9783527618156

S. P. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Foundations and Trends?? in Machine Learning, vol.3, issue.1
DOI : 10.1561/2200000016

S. P. Boyd and L. Vandenberghe, Convex Optimization, 2004.

L. Bregman, The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming, USSR Computational Mathematics and Mathematical Physics, vol.7, issue.3, 1966.
DOI : 10.1016/0041-5553(67)90040-7

C. Brucker, 3D scanning PIV applied to an air flow in a motored engine using digital high-speed video, Measurement Science and Technology, vol.8, issue.12, 1997.
DOI : 10.1088/0957-0233/8/12/011

A. M. Bruckstein, M. Elad, and M. Zibulevsky, On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations, IEEE Transactions on Information Theory, vol.54, issue.11, p.54, 2008.
DOI : 10.1109/TIT.2008.929920

S. Burgman, C. Brücker, and W. Schröder, Scanning PIV measurements of a laminar separation bubble, Experiments in Fluids, vol.397, issue.8, 2006.
DOI : 10.1007/s00348-006-0153-6

C. L. Byrne, Iterative Image Reconstruction Algorithms Based on Cross-Entropy Minimization, IEEE Trans. Image Process, vol.2, issue.1, 1993.

E. J. Candès, The restricted isometry property and its implications for compressed sensing, Comptes Rendus Mathematique, vol.346, issue.9-10, pp.9-10, 2008.
DOI : 10.1016/j.crma.2008.03.014

E. J. Candès and J. Romberg, 1 -magic recovery of sparse signals via convex programming, California Inst. Technol, 2005.

E. J. Candès and T. Tao, Decoding by Linear Programming, IEEE Transactions on Information Theory, vol.51, issue.12, p.51, 2005.
DOI : 10.1109/TIT.2005.858979

E. J. Candès, M. B. Wakin, and S. P. Boyd, Enhancing Sparsity by Reweighted ??? 1 Minimization, Journal of Fourier Analysis and Applications, vol.7, issue.3, 2008.
DOI : 10.1007/s00041-008-9045-x

Y. Censor, Row-Action Methods for Huge and Sparse Systems and Their Applications, SIAM Review, vol.23, issue.4, 1981.
DOI : 10.1137/1023097

Y. Censor, Finite series-expansion reconstruction methods, Proc. IEEE, 1983.
DOI : 10.1109/PROC.1983.12598

Y. Censor and T. Elfving, Block-Iterative Algorithms with Diagonally Scaled Oblique Projections for the Linear Feasibility Problem, SIAM Journal on Matrix Analysis and Applications, vol.24, issue.1, 2002.
DOI : 10.1137/S089547980138705X

Y. A. Censor and S. A. Zenios, Parallel Optimization: Theory, Algorithms and Applications, 1997.

F. Champagnat, P. Cornic, A. Cheminet, B. Leclaire, and G. Besnerais, Tomographic PIV: particles vs blobs, PIV, 2013.
DOI : 10.1088/0957-0233/25/8/084002

V. Chandar, A Negative Result Concerning Explicit Matrices With The Restricted Isometry Property, 2008.

A. Cheminet, B. Leclaire, F. Champagnat, P. Cornic, and G. Besnerais, On factors affecting the quality of tomographic reconstruction, PIV, 2013.

S. Chen, C. F. Cowan, and P. M. Grant, Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks, vol.2, issue.2, 1991.
DOI : 10.1109/72.80341

G. Cimmino, Calcolo approsimato per le soluzioni dei sistemi di equazioni lineari, La Ric. Sci, vol.14, issue.2, 1938.

S. Coëtmellec, C. Buraga-lefebvre, D. Lebrun, and C. Özkul, Application of in-line digital holography to multiple plane velocimetry, Measurement Science and Technology, vol.12, issue.9, 2001.
DOI : 10.1088/0957-0233/12/9/303

P. Cornic, F. Champagnat, A. Cheminet, B. Leclaire, and G. Besnerais, Computationally efficient sparse algorithms for tomographic PIV Reconstruction, PIV, 2013.

T. Corpetti, É. Mémin, and P. Perez, Dense estimation of fluid flows, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.3
DOI : 10.1109/34.990137
URL : https://hal.archives-ouvertes.fr/hal-00329724

C. Couvreur and Y. Bresler, On the Optimality of the Backward Greedy Algorithm for the Subset Selection Problem, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.3, 2000.
DOI : 10.1137/S0895479898332928

A. Cuzol and É. Mémin, Vortex and Source Particles for Fluid Motion Estimation, Lect. Notes Comput. Sc, vol.3459, 2005.
DOI : 10.1007/11408031_22
URL : https://hal.archives-ouvertes.fr/hal-00386463

W. Dai and O. Milenkovic, Subspace Pursuit for Compressive Sensing: Closing the Gap Between Performance and Complexity, 2008.

C. M. De-silva, R. Baidya, M. Khashehchi, and I. Marusic, Assessment of tomographic PIV in wall-bounded turbulence using direct numerical simulation data, Experiments in Fluids, vol.49, issue.12, pp.52-2012
DOI : 10.1007/s00348-011-1227-7

S. Discetti and T. Astarita, Acceleration of tomo-PIV by multigrid reconstruction schemes, SALTFM, 2010.

D. L. Donoho, Sparse Components of Images and Optimal Atomic Decompositions, Constructive Approximation, vol.17, issue.3, 2001.
DOI : 10.1007/s003650010032

D. L. Donoho, M. Elad, D. L. Donoho, and J. Tanner, Optimally sparse representation in general (nonorthogonal) dictionaries via ??1 minimization, Proc. Natl. Acad. Sci. USA Sparse Nonnegative Solution of Underdetermined Linear Equations by Linear Programming. Proc. Natl. Acad. Sci. USA, p.102, 2003.
DOI : 10.1073/pnas.0437847100

D. L. Donoho and Y. Tsaig, Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit, IEEE Transactions on Information Theory, vol.58, issue.2, 2006.
DOI : 10.1109/TIT.2011.2173241

D. L. Donoho and Y. Tsaig, Fast Solution of 1 -norm Minimization Problems When the Solution May be Sparse, IEEE Trans. Inf. Theory, issue.11, p.54, 2008.

J. Eckstein and D. P. Bertsekas, On the Douglas???Rachford splitting method and the proximal point algorithm for maximal monotone operators, Mathematical Programming, vol.29, issue.1, 1992.
DOI : 10.1007/BF01581204

M. Edmunds, R. S. Laramee, G. Chen, N. Max, E. Zhang et al., Surface-based flow visualization. Computers and Graphics, 2012.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least Angle Regression, Ann. Stat, vol.32, 2004.

M. Elad and A. M. Bruckstein, A generalized uncertainty principle and sparse representation in pairs of bases, IEEE Transactions on Information Theory, vol.48, issue.9, p.48, 2002.
DOI : 10.1109/TIT.2002.801410

T. Elfving, On some methods for entropy maximization and matrix scaling, Linear Algebra and its Applications, vol.34, issue.12, 1980.
DOI : 10.1016/0024-3795(80)90171-8

T. Elfving, T. Nikazad, and C. Hansen, Semi-convergence and relaxation parameters for a class of SIRT algorithms, SIAM J. Imaging Sci

G. Elsinga, F. Scarano, B. Wieneke, and B. Van-oudheusden, Tomographic particle image velocimetry, Experiments in Fluids, vol.28, issue.7, p.41, 2006.
DOI : 10.1007/s00348-006-0212-z

G. E. Elsinga, Complete removal of ghost particles in Tomographic-PIV, PIV, 2013.

G. E. Elsinga, F. Scarano, B. Wieneke, and B. W. Oudheusden, Assemenent of tomo-PIV for three-dimensional flows, PIV, 2005.

G. E. Elsinga, F. Scarano, B. Wieneke, and B. W. Oudheusden, Tomographic particle image velocimetry, PIV, 2005.
DOI : 10.1007/s00348-006-0212-z

G. E. Elsinga, J. Westerweel, F. Scarano, and M. Novara, On the velocity of ghost particles and the bias errors in Tomographic-PIV, Experiments in Fluids, vol.45, issue.4, 2010.
DOI : 10.1007/s00348-010-0930-0

G. E. Elsinga, B. Wieneke, F. Scarano, and A. Schröder, Tomographic 3D-PIV and Applications, Top. Appl. Phys, vol.112, 2008.
DOI : 10.1007/978-3-540-73528-1_6

T. Fahringer and B. S. Thurow, Tomographic Reconstruction of a 3-D Flow Field Using a Plenoptic Camera, 42nd AIAA Fluid Dynamics Conference and Exhibit, 2012.
DOI : 10.2514/6.2012-2826

D. Fleet and Y. Weiss, Optical Flow Estimation, Handbook of Mathematical Models in Computer Vision, chapter 15, 2006.
DOI : 10.1007/0-387-28831-7_15

T. Georgiev, New results on the Plenoptic 2.0 camera, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers, 2009.
DOI : 10.1109/ACSSC.2009.5469965

D. German, Constrained restoration and the recovery of discontinuities, IEEE Trans. Pattern Anal. Mach. Intell, vol.14, issue.3, 1992.

S. Ghaemi and F. Scarano, Multi-pass light amplification for tomographic particle image velocimetry applications, Measurement Science and Technology, vol.21, issue.12, 2010.
DOI : 10.1088/0957-0233/21/12/127002

R. Gribonval and M. Nielsen, Sparse representations in unions of bases, IEEE Transactions on Information Theory, vol.49, issue.12, 2002.
DOI : 10.1109/TIT.2003.820031
URL : https://hal.archives-ouvertes.fr/inria-00071943

R. L. Grothe and D. Dabiri, An improved three-dimensional characterization of defocusing digital particle image velocimetry (DDPIV) based on a new imaging volume definition, Measurement Science and Technology, vol.19, issue.6, 2008.
DOI : 10.1088/0957-0233/19/6/065402

C. Hansen and M. Saxild-hansen, AIR Tools ??? A MATLAB package of algebraic iterative reconstruction methods, Journal of Computational and Applied Mathematics, vol.236, issue.8, p.2012
DOI : 10.1016/j.cam.2011.09.039

P. Héas, É. Mémin, D. Heitz, and P. D. Mininni, Power laws and inverse motion modelling: application to turbulence measurements from satellite images, Tellus A, vol.60, issue.00, 2012.
DOI : 10.1007/s10851-007-0014-9

G. T. Herman-jr, Application of Maximum Entropy and Bayesian Optimization Methods to Image Reconstruction from Projections, Maximum Entropy and Bayesian Methods in Inverse, 1985.
DOI : 10.1007/978-94-017-2221-6_14

G. T. Herman and A. Lent, Iterative reconstruction algorithms, Computers in Biology and Medicine, vol.6, issue.4, 1976.
DOI : 10.1016/0010-4825(76)90066-4

G. T. Herman, A. Lent, and P. H. Lutz, Relaxation methods for image reconstruction, Communications of the ACM, vol.21, issue.2, 1978.
DOI : 10.1145/359340.359351
URL : http://scholarworks.rit.edu/cgi/viewcontent.cgi?article=1990&context=article

G. T. Herman and L. B. Meyer, Algebraic reconstruction techniques can be made computationally efficient, IEEE Trans. Med. Imag, vol.12, issue.3, 1993.

C. Herzet and A. Drémeau, Bayesian Pursuit Algorithms, EUSIPCO, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00673801

K. D. Hinsch, Three-dimensional particle velocimetry, Meas. Sci. Technol, vol.6, issue.6, 1995.

K. D. Hinsch, Holographic particle image velocimetry Determining Optical Flow, MEAS SCI TECHNOL Artif. Intell, vol.17, pp.1-3, 1981.

P. J. Huber, Robust Estimation of a Location Parameter, Annals of Statistics, vol.53, 1964.

M. Jiang and G. Wang, Convergence of the Simultaneous Algebraic Reconstruction Technique (SART), IEEE Trans. Image Process, vol.12, issue.8, 2003.

S. Kaczmarz, Angenäherte Auflösung von Systemen Linearer Gleichungen, J. Theor. Biol, vol.35, 1937.

S. and K. Harouna, Ondelettes pour la prise en compte de conditions aux limites en turbulence incompressible, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00544373

C. Kähler and J. Kompenhaus, Fundamentals of multiple plane stereo particle image velocimetry, Experiments in Fluids, vol.29, issue.7, 2000.
DOI : 10.1007/s003480070009

C. Kähler, B. Sammler, and J. Kompenhaus, Generation and control of particle size distributions for optical velocity measurement techniques in fluid mechanics, PIV, 2002.

L. Kajitani and D. Dabiri, A full three-dimensional characterization of defocusing digital particle image velocimetry, Meas. Sci. Technol, vol.16, issue.3, 2005.

]. N. Karmarkar, A new polynomial-time algorithm for linear programming

J. Kitzhofer, P. Westfeld, O. Pust, H. G. Nonn, and C. Brucker, Estimation of 3D deformation and rotation rate tensor from volumetric particle data via 3D least squares matching, SALTFM, 2010.

M. Kojima, N. Megiddo, and S. Mizuno, Theoretical convergence of large-step primal???dual interior point algorithms for linear programming, Mathematical Programming, vol.50, issue.1-3, 1993.
DOI : 10.1007/BF01581234

S. Kullback and R. Leibler, On Information and Sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, 1951.
DOI : 10.1214/aoms/1177729694

R. R. , L. Foy, and P. Vlachos, Multi-Camera Plenoptic Particle Image Velocimetry, PIV, 2013.

Y. , L. Sant, F. Champagnat, G. Besnerais, B. Jaubert et al., Folki-3C : un algorithme d'extraction directe de champs 3C en PIV, CFM, 2009.

A. Lent and Y. Censor, The primal-dual algorithm as a constraint-set-manipulation device, Mathematical Programming, vol.7, issue.1-3, pp.1-3, 1991.
DOI : 10.1007/BF01594943

B. D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, Proc. Imaging Understanding Workshop, 1981.

K. Lynch and B. S. Thurow, Three-Dimensional Particle Image Velocimetry Using a Plenoptic Camera, 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 2012.
DOI : 10.2514/6.2012-1056

H. G. Maas, A. Gruen, and D. Papantoniou, Particle tracking velocimetry in three-dimensional flows, Experiments in Fluids, vol.15, issue.2, 1993.
DOI : 10.1007/BF00190953

D. M. Maliatouv, M. Çetin, and A. Willsky, Homotopy continuation for sparse signal representation, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005.
DOI : 10.1109/ICASSP.2005.1416408

S. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, p.41, 1993.
DOI : 10.1109/78.258082

C. Mätzler, MATLAB Functions for Mie Scattering and Absorbtion, 2002.

N. Megiddo, Pathways to the Optimal Set in Linear Programming, Progress in Mathematical Programming: Interior Point and Related Methods, 1989.
DOI : 10.1007/978-1-4613-9617-8_8

A. Melling, Tracer particles and seeding for particle image velocimetry, Measurement Science and Technology, vol.8, issue.12, 1997.
DOI : 10.1088/0957-0233/8/12/005

H. Meng, G. Pan, Y. Pu, and S. H. Woordward, Holographic particle image velocimetry: from film to digital recording, Measurement Science and Technology, vol.15, issue.4, 2004.
DOI : 10.1088/0957-0233/15/4/009

M. Merzkirch, Techniques of Flow Visualization, AGARD, 1987.

R. Meynart, Digital image processing for speckle flow velocimetry, Review of Scientific Instruments, vol.53, issue.1, 1982.
DOI : 10.1063/1.1136808

R. Meynart, Speckle velocimetry: an application of image analysis techniques to the measurement of instantaneous velocity fields in unsteady flow, ICIASF, 1983.

A. Mitiche and P. Bouthemy, Computation and analysis of image motion: A synopsis of current problems and methods, International Journal of Computer Vision, vol.7, issue.4, 1996.
DOI : 10.1007/BF00131147

R. D. Monteiro and I. Adler, Interior path following primal-dual algorithms. part I: Linear programming, Mathematical Programming, vol.40, issue.1-3, 1989.
DOI : 10.1007/BF01587075

Y. Nakayama, W. A. Woods, and D. G. Clark, Visualized Flow, 1993.

D. Needell and J. A. Tropp, CoSaMP: Iterative signal recovery from incomplete and inaccurate samples, Applied and Computational Harmonic Analysis, vol.26, issue.3, 2009.
DOI : 10.1016/j.acha.2008.07.002

Y. Nesterov, A method for solving the convex programming problem with convergence rate O( (k)), Dokl. Akad. Nauk. SSSR, vol.269, 1986.

M. Novara, Advances in tomographic PIV, 2013.

M. Novara, K. J. Batenburg, and F. Scarano, Motion tracking-enhanced MART for tomographic PIV, Measurement Science and Technology, vol.21, issue.3, 2010.
DOI : 10.1088/0957-0233/21/3/035401

M. Osborne, B. Presnell, B. T. Parikh, and S. P. Boyd, A new approach to variable selection in least squares problems, Proximal Algorithms. Found. Trends Optim., 2013. [137] Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad. Orthogonal Matching Pursuit: Recursive Function Approximation with Applications to Wavelet Decomposition. In ASILOMAR, 1993.
DOI : 10.1093/imanum/20.3.389

F. Pereira, M. Gharib, D. Dabiri, and D. Modaress, Defocusing digital particle image velocimetry: a 3-component 3-dimensional DPIV measurement technique. Application to bubbly flows, Experiments in Fluids, vol.29, issue.7, 2000.
DOI : 10.1007/s003480070010

S. Petra, C. Popa, and C. Schnörr, Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods, VMM, 2008.

S. Petra, C. Popa, and C. Schnörr, Extended and Constrained Cimmino-type Algorithms with Applications in Tomographic Image Reconstruciton, Int. J. Comput. Math, 2010.

S. Petra and C. Schnörr, Average case recovery analysis of tomographic compressive sensing. Special Issue on Sparse Approximate Solution of Linear Systems, p.441, 2013.

S. Petra, C. Schnörr, F. Becker, and F. Lenzen, B-SMART: Bregman-Based First-Order Algorithms for Non-negative Compressed Sensing Problems, SSVM, 2013.
DOI : 10.1007/978-3-642-38267-3_10

S. Petra, C. Schnörr, A. Schröder, and B. Wieneke, Tomographic Image Reconstruction in Experimental Fluid Dynamics: Synopsis and Problems, WMM, 2007.

S. Petra and C. Schnörr, TomoPIV Meets Compressed Sensing, Pure Math. Appl, 2009.
DOI : 10.1063/1.3498196

S. Petra, A. Schröder, and C. Schnörr, 3D Tomography from Few Projections in Experimental Fluid Mechanics editor, Imaging Measurement Methods for Flow Analysis, 2009.

S. Petra, A. Schröder, and C. Schnörr, Critical parameter values and reconstruction properties of discrete tomography: Application to experimental fluid dynamics

S. Petra, A. Schröder, B. Wieneke, and C. Schnörr, On Sparsity Maximization in Tomographic Particle Image Reconstruction, Proceedings of the 30th DAGM Symposium on Pattern Recognition, 2008.
DOI : 10.1007/978-3-540-69321-5_30

S. Petra, A. Schröder, B. Wieneke, and C. Schnörr, 3D Tomography from Few Projections in Experimental Fluid Dynamics, Imaging Measurement Methods for Flow Analysis, 2009.
DOI : 10.1007/978-3-642-01106-1_7

B. T. Phong, Illumination for computer generated pictures, Communications of the ACM, vol.18, issue.6, 1975.
DOI : 10.1145/360825.360839

J. Ponce and D. A. Forsyth, Computer Vision: A Modern Approach, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01063327

C. Popa, Constrained Kaczmarz extended algorithm for image reconstruction, Linear Algebra and its Applications, vol.429, issue.8-9, 2008.
DOI : 10.1016/j.laa.2008.06.024

M. Raffel, E. Christian, S. Wereley, and J. Kompenhaus, Particle Image Velocimery ? A Practical Guide, 2007.

M. A. Saunders, PDCO: Primal-dual interior-point method for convex objectives, Syst. Optim. Lab, 2002.

. Scarano, Recent Developments in time-resolved three dimensional velocity measurements in turbulent flows by high-speed tomographic PIV, FVR, 2011.

F. Scarano, Tomographic PIV: principles and practice, Measurement Science and Technology, vol.24, issue.1, p.2013
DOI : 10.1088/0957-0233/24/1/012001

F. Scarano and B. W. Oudheusden, Planar velocity measurements of a two-dimensional compressible wake, Experiments in Fluids, vol.8, issue.3, 2003.
DOI : 10.1007/s00348-002-0581-x

F. Scarano and C. Poelma, Three-dimensional vorticity patterns of cylinder wakes, Experiments in Fluids, vol.8, issue.4
DOI : 10.1007/s00348-009-0629-2

J. Schäfer, Implementierung und Anwendung analytischer und numerischer Verfahren zur Losung der Maxwellgleichungen fur die Untersuchung der Lichtausbreitung in biologischem Gewebe, 2011.

L. Schäfer and A. Schröder, Comparison of Holographic and Tomographic Particle-Image Velocimetry Turbulent Channel Flow Measurements, ETC, 2013.
DOI : 10.1088/1742-6596/318/2/022019

D. Schanz, S. Gesemann, A. Schroder, B. Wieneke, and D. Michaelis, Tomographic reconstruction with non-uniform optical transfert functions (OTF), SALTFM, 2010.

D. Schanz, S. Gesemann, A. Schröder, B. Wieneke, and M. Novara, Non-uniform optical transfer functions in particle imaging: calibration and application to tomographic reconstruction, Measurement Science and Technology, vol.24, issue.2, p.2013
DOI : 10.1088/0957-0233/24/2/024009

A. Schröder, R. Geisler, G. E. Elsinga, F. Scarano, and U. Dierksheide, Investigation of a turbulent spot and a tripped turbulent boundary layer flow using time-resolved tomographic PIV, Experiments in Fluids, vol.387, issue.2, p.44, 2008.
DOI : 10.1007/s00348-007-0403-2

G. S. Settles, E. B. Hackett, J. D. Miller, and L. Weinstein, Full-Scale Schlieren Flow Visualization. Flow Visualization, 1995.

C. M. Silva, R. Baidya, and I. Marusic, Enhancing Tomo-PIV reconstruction quality by reducing ghost particles, Measurement Science and Technology, vol.24, issue.2, p.2012
DOI : 10.1088/0957-0233/24/2/024010

K. D. Solf, Fotografie: Grundlagen, 1986.

S. M. Soloff and R. J. Adrian, Distortion compensation for generalized stereoscopic particle image velocimetry, Measurement Science and Technology, vol.8, issue.12, 1997.
DOI : 10.1088/0957-0233/8/12/008

C. Soussen, J. Idier, D. Brie, and J. Duan, From Bernoulli–Gaussian Deconvolution to Sparse Signal Restoration, IEEE Transactions on Signal Processing, vol.59, issue.10, p.59, 2011.
DOI : 10.1109/TSP.2011.2160633

D. Suter, L. Thomas, B. Tremblais, and L. David, Motion estimation and vector splines Influence des paramètres de reconstruction sur la qualité des résultats de tomo-PIV, CCVPR CTFL, 1994.

L. Thomas, R. Vernet, B. Tremblais, and L. David, Influence of geometric parameters and image preprocessing on tomo-piv results, SALTFM, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00874856

B. S. Thurow and T. Fahringer, Recent Development of Volumetric PIV with a Plenoptic Camera, PIV, 2013.

B. Triggs, P. Mclauchlan, R. Hartley, and A. Fitzgibbon, Bundle Adjustment ?A Modern Synthesis. Vision Algorithms: Theory and Practice, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00548290

C. Tropea, A. L. Yarin, and J. F. Foss, Handbook of Experimental Fluid Mechanics, 2007.

J. A. Tropp, Greed is Good: Algorithmic Results for Sparse Approximation, IEEE Transactions on Information Theory, vol.50, issue.10, 2004.
DOI : 10.1109/TIT.2004.834793

J. A. Tropp and S. J. Wright, Computational Methods for Sparse Solution of Linear Inverse Problems, Proc. IEEE, p.98, 2010.
DOI : 10.1109/JPROC.2010.2044010

]. P. Tseng, On Accelerated Proximal Gradient Methods for Convex-Concave Optimization . submitted to, SIAM J. Optim, 2008.

B. Turlach, On Algorithms for Solving Least Squares Problems under an 1 Penalty or an 1 Constraint, ASA Proc. Stat. Comput. Sec, 2005.

M. Van-dyke, An album of fluid motion, 1982.

S. Vedula, S. Baker, P. Rander, R. T. Collins, and T. Kanade, Three-Dimensional Scene Flow, ICCV, 1999.

C. Vogel, Computation Methods for Inverse Problems, Society for Industrial and Applied Mathematics Philadelphia, 2002.

P. Westfeld, H. Maas, O. Pust, J. Kitzhofer, and C. Brucker, 3-D least squares matching for volumetric velocimetry data processing, SALTFM, 2010.

B. Wieneke, Volume self-calibration for 3D particle image velocimetry, Experiments in Fluids, vol.8, issue.4, 2008.
DOI : 10.1007/s00348-008-0521-5

B. Wieneke, Iterative reconstruction of volumetric particle distribution, Measurement Science and Technology, vol.24, issue.2, p.2013
DOI : 10.1088/0957-0233/24/2/024008

C. E. Willert, Stereoscopic digital particle image velocimetry for application in wind tunnel flows, Measurement Science and Technology, vol.8, issue.12, 1997.
DOI : 10.1088/0957-0233/8/12/010

C. E. Willert and M. Gharib, Digital particle image velocimetry, Experiments in Fluids, vol.10, issue.4, 1991.
DOI : 10.1007/BF00190388

N. A. Worth and T. B. Nickels, Acceleration of Tomo-PIV by estimating the initial volume intensity distribution, Experiments in Fluids, vol.12, issue.5, p.45, 2008.
DOI : 10.1007/s00348-008-0504-6

T. Wriedt, A Review of Elastic Light Scattering Theories, Particle & Particle Systems Characterization, vol.15, issue.2, 1998.
DOI : 10.1002/(SICI)1521-4117(199804)15:2<67::AID-PPSC67>3.0.CO;2-F

S. J. Wright, Primal-Dual Interior-Point Methods. SIAM, 1997.

A. Y. Yang, A. Ganesh, Z. Zhou, Y. Sastry, and S. S. Ma, A Review of Fast 1 ?Minimization Algorithms for Robust Face Recognition, 2010.

Z. Zhang, Camera Calibration In Emerging Topics in Computer Vision, 2004.