D. J. Brenner and E. J. Hall, Computed Tomography ??? An Increasing Source of Radiation Exposure, New England Journal of Medicine, vol.357, issue.22, pp.2277-2284, 2007.
DOI : 10.1056/NEJMra072149

Y. Chen and W. F. Chen, Improving low-dose abdominal CT images by Weighted Intensity Averaging over Large-scale Neighborhoods, European Journal of Radiology, vol.80, issue.2, pp.42-49, 2011.
DOI : 10.1016/j.ejrad.2010.07.003

Y. Chen, Z. Yang, Y. N. Hu, G. Y. Yang, L. M. Luo et al., Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means, Physics in Medicine and Biology, vol.57, issue.9, pp.2667-2688, 2012.
DOI : 10.1088/0031-9155/57/9/2667

URL : https://hal.archives-ouvertes.fr/inserm-00677979

Y. S. Li, Y. Chen, and L. M. Luo, Strategy of computed tomography sinogram inpainting based on sinusoid-like curve decomposition and eigenvector-guided interpolation, Journal of the Optical Society of America A, vol.29, issue.1, pp.153-163, 2012.
DOI : 10.1364/JOSAA.29.000153

URL : https://hal.archives-ouvertes.fr/inserm-00677987

E. Y. Sidky, C. M. Kao, and X. Pan, Accurate Image Reconstruction from Few-Views and Limited- Angle Data in Divergent-Beam CT, J.X-ray Sci. Tech, vol.14, pp.119-139, 2006.

Y. Chen, Q. Feng, L. M. Luo, P. Shi, and W. Chen, Nonlocal Prior Bayesian Tomographic Reconstruction, Journal of Mathematical Imaging and Vision, vol.4, issue.4, pp.133-146, 2008.
DOI : 10.1007/s10851-007-0042-5

J. Tang, B. Nett, and G. Chen, Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms, Physics in Medicine and Biology, vol.54, issue.19, pp.5781-5804, 2009.
DOI : 10.1088/0031-9155/54/19/008

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3354336

J. C. Carr, W. R. Fright, and R. K. Beatson, Surface interpolation with radial basis functions for medical imaging, IEEE Transactions on Medical Imaging, vol.16, issue.1, pp.96-107, 1997.
DOI : 10.1109/42.552059

H. Kostler, M. Prummer, and U. Rude, Adaptive variational sinogram interpolation of sparsely sampled CT data, 18th International Conference on Pattern Recognition (ICPR'06), pp.778-781, 2006.
DOI : 10.1109/ICPR.2006.225

M. S. Lewic, B. A. Olshausen, and D. J. Field, Emergence of Simple-cell Receptive Field Properties by Learning a Sparse Code for Natural Images, Nature, vol.381, pp.607-609, 1996.

M. S. Lewicki, Learning Overcomplete Representations, Neural Computation, vol.33, issue.2, pp.337-365, 2000.
DOI : 10.1109/18.119725

K. K. Delgado and J. F. Murray, Dictionary Learning Algorithms for Sparse Representation, Neural Computation, vol.15, issue.2, pp.349-396, 2003.
DOI : 10.1162/089976601300014385

D. L. Donoho and M. 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.
DOI : 10.1073/pnas.0437847100

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.
DOI : 10.1109/TIP.2006.881969

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.109.6477

M. Aharon, M. Elad, and A. M. Bruckstein, <tex>$rm K$</tex>-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.
DOI : 10.1109/TSP.2006.881199

J. Mairal, G. Sapiro, and M. Elad, Learning Multiscale Sparse Representations for Image and Video Restoration, Multiscale Modeling & Simulation, vol.7, issue.1, pp.214-241, 2008.
DOI : 10.1137/070697653

M. J. Fadili, J. L. Starck, and F. Murtagh, Inpainting and Zooming Using Sparse Representations, The Computer Journal, vol.52, issue.1, pp.64-79, 2009.
DOI : 10.1093/comjnl/bxm055

URL : https://hal.archives-ouvertes.fr/hal-00091746

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, Robust Face Recognition via Sparse Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.2, pp.210-227, 2008.
DOI : 10.1109/TPAMI.2008.79

S. Ravishankar and Y. Bresler, MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning, IEEE Transactions on Medical Imaging, vol.30, issue.5, pp.1028-1041, 2012.
DOI : 10.1109/TMI.2010.2090538

Q. Xu, H. Y. Yu, X. Q. Mou, L. Zhang, J. Hsieh et al., Low-Dose X-ray CT Reconstruction via Dictionary Learning, IEEE Trans. Med. Imag, vol.31, pp.1682-1697, 2012.

S. Li, L. Fang, and H. Yin, An Efficient Dictionary Learning Algorithm and Its Application to 3-D Medical Image Denoising, IEEE Trans. Biomed. Eng, vol.59, pp.417-427, 2012.

J. Shtok, M. Elad, and M. Zibulevsky, Sparsity-Based Sinogram Denosing for Low-Dose Computed Tomography, International Conference on Acoustics, Speech and Signal Processing, pp.22-27, 2011.

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging, 1998.

V. N. Temlyakov, Greedy Algorithms andM-Term Approximation with Regard to Redundant Dictionaries, Journal of Approximation Theory, vol.98, issue.1, pp.117-145, 1999.
DOI : 10.1006/jath.1998.3265

E. B. Dam, M. Koch, and M. Lillholm, Quaternion Interpolation and Animation, 1998.

Z. Wang, A. C. Bovik, and H. R. Sheikh, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.2477