S. Borman and R. Stevenson, patial resolution enhancement of lowresolution image sequences. A comprehensive review with directions for future research Lab. Image and Signal Analysis, Univ. Notre Dame, 1998.

S. C. Park, M. K. Park, and M. G. Kang, Super-resolution image reconstruction: a technical overview, IEEE Signal Processing Magazine, vol.20, issue.3, pp.21-36, 2003.
DOI : 10.1109/MSP.2003.1203207

J. Tian and K. Ma, A survey on super-resolution imaging, Signal, Image Video Process, pp.329-342, 2011.
DOI : 10.1007/s11760-010-0204-6

L. Ziwei, W. Chengdong, C. Dongyue, Q. Yuanchen, and W. Chunping, Overview on image super resolution reconstruction, The 26th Chinese Control and Decision Conference (2014 CCDC), pp.2009-2014, 2014.
DOI : 10.1109/CCDC.2014.6852498

S. Vishnukumar, M. S. Nair, and M. Wilscy, Edge preserving single image super-resolution with improved visual quality, Signal Processing, vol.105, pp.283-297, 2014.
DOI : 10.1016/j.sigpro.2014.05.033

M. Elad and A. Feuer, Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images, IEEE Transactions on Image Processing, vol.6, issue.12, pp.1646-1658, 1997.
DOI : 10.1109/83.650118

S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multiframe Super Resolution, IEEE Transactions on Image Processing, vol.13, issue.10, pp.1327-1344, 2004.
DOI : 10.1109/TIP.2004.834669

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

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Advances and challenges in super-resolution, International Journal of Imaging Systems and Technology, vol.19, issue.2, pp.47-57, 2004.
DOI : 10.1002/ima.20007

N. Nguyen, P. Milanfar, and G. Golub, Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement, IEEE Transactions on Image Processing, vol.10, issue.9, pp.1299-1308, 2001.
DOI : 10.1109/83.941854

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

M. E. Tipping and C. M. Bishop, Bayesian image super-resolution, Proc. NIPS, pp.1279-1286, 2002.

N. Efrat, D. Glasner, A. Apartsin, B. Nadler, and A. Levin, Accurate Blur Models vs. Image Priors in Single Image Super-resolution, 2013 IEEE International Conference on Computer Vision, pp.2832-2839, 2013.
DOI : 10.1109/ICCV.2013.352

M. Irani and S. Peleg, Improving resolution by image registration, CVGIP: Graphical Models and Image Processing, vol.53, issue.3, pp.231-239, 1991.
DOI : 10.1016/1049-9652(91)90045-L

P. Milanfar, Super-Resolution Imaging, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00563891

M. S. Alam, J. G. Bognar, R. C. Hardie, and B. J. Yasuda, Infrared image registration and high-resolution reconstruction using multiple translationally shifted aliased video frames, IEEE Transactions on Instrumentation and Measurement, vol.49, issue.5, pp.915-923, 2000.
DOI : 10.1109/19.872908

S. Lertrattanapanich and N. K. Bose, High resolution image formation from low resolution frames using delaunay triangulation, IEEE Transactions on Image Processing, vol.11, issue.12, pp.1427-1441, 2002.
DOI : 10.1109/TIP.2002.806234

N. K. Bose and N. A. Ahuja, Superresolution and noise filtering using moving least squares, IEEE Transactions on Image Processing, vol.15, issue.8, pp.2239-2248, 2006.
DOI : 10.1109/TIP.2006.877406

N. Nguyen and P. Milanfar, A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution), Circuits Systems and Signal Processing, vol.54, issue.2, pp.321-338, 2000.
DOI : 10.1007/BF01200891

H. Ur and D. Gross, Improved resolution from subpixel shifted pictures, CVGIP: Graphical Models and Image Processing, vol.54, issue.2, pp.181-186, 1992.
DOI : 10.1016/1049-9652(92)90065-6

A. Papoulis, Signal Analysis, 1978.

D. C. Youla and H. Webb, Image Restoration by the Method of Convex Projections: Part 1ߞTheory, IEEE Transactions on Medical Imaging, vol.1, issue.2, pp.81-94, 1982.
DOI : 10.1109/TMI.1982.4307555

H. Stark, Theory of convex projection and its application to image restoration, 1988., IEEE International Symposium on Circuits and Systems, pp.963-964, 1988.
DOI : 10.1109/ISCAS.1988.15083

H. Stark and P. Oskoui, High-resolution image recovery from image-plane arrays, using convex projections, Journal of the Optical Society of America A, vol.6, issue.11, pp.1715-1726, 1989.
DOI : 10.1364/JOSAA.6.001715

A. Zomet and S. Peleg, Efficient super-resolution and applications to mosaics, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.579-583, 2000.
DOI : 10.1109/ICPR.2000.905404

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

A. Zomet, A. Rav-acha, and S. Peleg, Robust super-resolution, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.645-650, 2001.
DOI : 10.1109/CVPR.2001.990535

R. R. Schultz and R. L. Stevenson, Extraction of high-resolution frames from video sequences, IEEE Transactions on Image Processing, vol.5, issue.6, pp.996-1011, 1996.
DOI : 10.1109/83.503915

B. K. Gunturk and M. Gevrekci, High-resolution image reconstruction from multiple differently exposed images, IEEE Signal Processing Letters, vol.13, issue.4, pp.197-200, 2006.
DOI : 10.1109/LSP.2005.863693

L. C. Pickup, D. P. Capel, S. J. Roberts, and A. Zisserman, Bayesian Methods for Image Super-Resolution, The Computer Journal, vol.52, issue.1, pp.101-113, 2009.
DOI : 10.1093/comjnl/bxm091

J. Tian and K. Ma, Stochastic super-resolution image reconstruction, Journal of Visual Communication and Image Representation, vol.21, issue.3, pp.232-244, 2010.
DOI : 10.1016/j.jvcir.2010.01.001

R. Y. Tsai and T. S. Huang, Multiframe image restoration and registration, Adv. Comput. Vis. Image Process, vol.1, issue.2, pp.317-339, 1984.

S. P. Kim, N. K. Bose, and H. M. Valenzuela, Recursive reconstruction of high resolution image from noisy undersampled multiframes, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.38, issue.6, pp.1013-1027, 1990.
DOI : 10.1109/29.56062

S. P. Kim and W. Su, Recursive high-resolution reconstruction of blurred multiframe images, IEEE Transactions on Image Processing, vol.2, issue.4, pp.534-539, 1993.
DOI : 10.1109/83.242363

R. R. Makwana and N. D. Mehta, Single image super-resolution via iterative back projection based canny edge detection and a Gabor filter prior, Int. J. Soft Comput., Eng, vol.3, issue.1, pp.2231-2307, 2013.

G. Daniel, B. Shai, and I. Michal, Super-resolution from a single image, Proc. IEEE 12th Int. Conf. Comput. Vis, pp.349-356, 2009.

W. T. Freeman, E. C. Pasztor, and O. T. Carmichael, Learning low-level vision, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.25-47, 2000.
DOI : 10.1109/ICCV.1999.790414

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

S. Baker and T. Kanade, Limits on super-resolution and how to break them, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.9, pp.1167-1183, 2002.
DOI : 10.1109/TPAMI.2002.1033210

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

W. T. Freeman, T. R. Jones, and E. C. Pasztor, Example-based super-resolution, IEEE Computer Graphics and Applications, vol.22, issue.2, pp.56-65, 2002.
DOI : 10.1109/38.988747

L. An and B. Bhanu, Face image super-resolution using 2D CCA, Signal Processing, vol.103, pp.184-194, 2014.
DOI : 10.1016/j.sigpro.2013.10.004

Q. Jianping, L. Ju, and C. Yen-wei, Joint blind super-resolution and shadow removing, IEICE Trans. Inf. Syst, vol.90, issue.12, pp.2060-2069, 2007.

L. Zhouchen, H. Junfeng, T. Xiaoou, and T. Chi-keung, Limits of learning-based superresolution algorithms, Int. J. Comput. Vis, vol.80, issue.3, pp.406-420, 2008.

A. Giachetti and N. Asuni, Real-Time Artifact-Free Image Upscaling, IEEE Transactions on Image Processing, vol.20, issue.10, pp.2760-2768, 2011.
DOI : 10.1109/TIP.2011.2136352

J. Yang, J. Wright, T. S. Huang, and Y. Ma, Image Super-Resolution Via Sparse Representation, IEEE Transactions on Image Processing, vol.19, issue.11, pp.2861-2873, 2010.
DOI : 10.1109/TIP.2010.2050625

C. Dong, C. C. Loy, K. He, and X. Tang, Image Super-Resolution Using Deep Convolutional Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.2, pp.295-307, 2016.
DOI : 10.1109/TPAMI.2015.2439281

URL : http://arxiv.org/abs/1501.00092

S. Kolouri and G. K. Rohde, Transport-based single frame super resolution of very low resolution face images, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4876-4884, 2015.
DOI : 10.1109/CVPR.2015.7299121

T. Michaeli and M. Irani, Nonparametric Blind Super-resolution, 2013 IEEE International Conference on Computer Vision, pp.945-952, 2013.
DOI : 10.1109/ICCV.2013.121

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

K. Loquin and O. Strauss, On the granularity of summative kernels, Fuzzy Sets and Systems, vol.159, issue.15, pp.1952-1972, 2008.
DOI : 10.1016/j.fss.2008.02.021

URL : https://hal.archives-ouvertes.fr/lirmm-00299387

F. Graba and O. Strauss, An interval-valued inversion of the non-additive interval-valued F-transform: Use for upsampling a signal, Fuzzy Sets and Systems, vol.288, pp.26-45, 2016.
DOI : 10.1016/j.fss.2015.08.020

URL : https://hal.archives-ouvertes.fr/lirmm-01278061

D. Denneberg, Non-Additive Measure and Integral, 1994.
DOI : 10.1007/978-94-017-2434-0

A. Rico and O. Strauss, Imprecise expectations for imprecise linear filtering, International Journal of Approximate Reasoning, vol.51, issue.8, pp.933-947, 2010.
DOI : 10.1016/j.ijar.2010.06.003

URL : https://hal.archives-ouvertes.fr/hal-00505515

D. Schmeidler, Integral representation without additivity Possibility theory and statistical reasoning, Process. Amer. Math. Soc. Comput. Statist. Data Anal, vol.97, issue.51 1, pp.255-261, 1986.

O. Strauss and A. Rico, Towards interval-based non-additive deconvolution in signal processing, Soft Computing, vol.16, issue.15, pp.809-820, 2012.
DOI : 10.1007/s00500-011-0771-7

URL : https://hal.archives-ouvertes.fr/lirmm-00857417

E. Ruspini, New experimental results in fuzzy clustering, Information Sciences, vol.6, pp.273-284, 1972.
DOI : 10.1016/0020-0255(73)90043-1

S. Markov, On directed interval arithmetic and its applications, Journal Universal Computer Science, pp.514-526, 1996.
DOI : 10.1007/978-3-642-80350-5_43

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

M. Hukuhara, Integration des applications mesurables dont la valeur est un compact convexe, Proc. Funkcial. Ekvac, pp.205-223, 1967.

J. Bouguet, Pyramidal implementation of the affine lucas Kanade feature tracker description of the algorithm, Intel Corp, 2000.

B. Silverman, Density Estimation for Statistics and Data Analysis, 1986.
DOI : 10.1007/978-1-4899-3324-9

M. V. Vandewalle and S. Süsstrunk, A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution, EURASIP Journal on Advances in Signal Processing, vol.2, issue.6, p.233, 2006.
DOI : 10.1155/ASP/2006/71459