Summarizing visual data using bidirectional similarity, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008. ,
DOI : 10.1109/CVPR.2008.4587842
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.2545
Inverse texture synthesis, in ACM Trans. Graphics (SIG- GRAPH), pp.1-9, 2008. ,
DOI : 10.1145/1399504.1360651
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.220.8528
Epitomic analysis of appearance and shape, Proceedings Ninth IEEE International Conference on Computer Vision, pp.34-41, 2003. ,
DOI : 10.1109/ICCV.2003.1238311
Video epitomes, Proc. IEEE Comp. Soc. Conf. Comp. Vis. Pattern Recogn, pp.42-49, 2005. ,
Clustering appearance and shape by learning jigsaws, Advances in Neural Information Process. Syst, pp.657-664, 2007. ,
Factoring repeated content within and among images, ACM Trans. Graphics (SIGGRAPH), 2008. ,
DOI : 10.1145/1399504.1360613
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.220.8575
Super-resolution from a single image, 2009 IEEE 12th International Conference on Computer Vision, pp.349-356, 2009. ,
DOI : 10.1109/ICCV.2009.5459271
Nonparametric blind superresolution, Proc. IEEE Int. Conf. Comp. Vis, pp.945-952, 2013. ,
Epitome-based image compression using translational sub-pel mapping, 2011 IEEE 13th International Workshop on Multimedia Signal Processing, pp.1-6, 2011. ,
DOI : 10.1109/MMSP.2011.6093786
URL : https://hal.archives-ouvertes.fr/hal-00749962
Epitome inpainting with in-loop residue coding for image compression, 2014 IEEE International Conference on Image Processing (ICIP), pp.5581-5585, 2014. ,
DOI : 10.1109/ICIP.2014.7026129
URL : https://hal.archives-ouvertes.fr/hal-00994418
A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2319-2323, 2000. ,
DOI : 10.1126/science.290.5500.2319
Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol.290, issue.5500, pp.2323-2326, 2000. ,
DOI : 10.1126/science.290.5500.2323
Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data, Proceedings of the National Academy of Sciences, vol.100, issue.10, pp.5591-5596, 2003. ,
DOI : 10.1073/pnas.1031596100
Super-resolution through neighbor embedding, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., pp.275-282, 2004. ,
DOI : 10.1109/CVPR.2004.1315043
Self-content superresolution for ultra-HD up-sampling, Proc. European Conf. Visual Media Prod, pp.49-58, 2012. ,
Image and video upscaling from local self-examples, ACM Transactions on Graphics, vol.30, issue.2, pp.1-10, 2010. ,
DOI : 10.1145/1944846.1944852
Optimized neighbor embeddings for single-image super-resolution, 2013 IEEE International Conference on Image Processing, pp.645-649, 2013. ,
DOI : 10.1109/ICIP.2013.6738133
Iterated neighborembeddings for image super-resolution, Proc. IEEE Int. Conf. Image Process, pp.3887-3891, 2014. ,
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
Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency, Journal of Visual Communication and Image Representation, vol.4, issue.4, pp.324-335, 1993. ,
DOI : 10.1006/jvci.1993.1030
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.7153