TY - GEN
T1 - Exemplar-based interpolation of sparsely sampled images
AU - Facciolo, Gabriele
AU - Arias, Pablo
AU - Caselles, Vicent
AU - Sapiro, Guillermo
PY - 2009
Y1 - 2009
N2 - A nonlocal variational formulation for interpolating a sparsely sampled image is introduced in this paper. The proposed variational formulation, originally motivated by image inpainting problems, encourages the transfer of information between similar image patches, following the paradigm of exemplar-based methods. Contrary to the classical inpainting problem, no complete patches are available from the sparse image samples, and the patch similarity criterion has to be redefined as here proposed. Initial experimental results with the proposed framework, at very low sampling densities, are very encouraging. We also explore some departures from the variational setting, showing a remarkable ability to recover textures at low sampling densities.
AB - A nonlocal variational formulation for interpolating a sparsely sampled image is introduced in this paper. The proposed variational formulation, originally motivated by image inpainting problems, encourages the transfer of information between similar image patches, following the paradigm of exemplar-based methods. Contrary to the classical inpainting problem, no complete patches are available from the sparse image samples, and the patch similarity criterion has to be redefined as here proposed. Initial experimental results with the proposed framework, at very low sampling densities, are very encouraging. We also explore some departures from the variational setting, showing a remarkable ability to recover textures at low sampling densities.
UR - https://www.scopus.com/pages/publications/70350616333
UR - https://www.scopus.com/inward/citedby.url?scp=70350616333&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03641-5_25
DO - 10.1007/978-3-642-03641-5_25
M3 - Conference contribution
AN - SCOPUS:70350616333
SN - 3642036406
SN - 9783642036408
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 331
EP - 344
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - 7th International Conference, EMMCVPR 2009, Proceedings
T2 - 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2009
Y2 - 24 August 2009 through 27 August 2009
ER -